Feature and case importance and confidence for imputation in computer-based reasoning systems

ABSTRACT

Techniques are provided for imputation in computer-based reasoning systems. The techniques include performing the following until there are no more cases in a computer-based reasoning model with missing fields for which imputation is desired: determining which cases have fields to impute (e.g., missing fields) in the computer-based reasoning model and determining conviction scores and/or imputation order information for the cases that have fields to impute. The techniques proceed by determining for which cases to impute data and, for each of the determined one or more cases with missing fields to impute data is imputed for the missing field, and the case is modified with the imputed data. Control of a system is then caused using the updated computer-based reasoning model.

PRIORITY CLAIM

The present application is a continuation-in-part of U.S. application Ser. No. 16/662,746 having a filing date of Oct. 24, 2019, which is a continuation-in-part of U.S. application Ser. No. 16/130,866 having a filing date of Sep. 13, 2018, each of which is incorporated by reference herein in its entirety.

FIELD OF THE INVENTION

The present invention relates to computer-based reasoning systems and more specifically to imputing data in computer-based reasoning systems.

BACKGROUND

Input or training data is incredibly important for computer-based reasoning systems. Even when there are sufficient cases (e.g., training data) to make a useful computer-based reasoning model, the data for each of the cases may lack some of the data items (fields) within the case. Consider, for example, a data set collected over a long period of time for oil pumps. Earlier oil pumps may have lacked many of the sensors and data collection mechanisms used on later oil pumps. Further, even if the older oil pumps are retrofitted with the sensors and data collection mechanisms used in more modern pumps, the data collected before those additional sensors and collection mechanisms will lack the data associated with those later-added sensors/mechanisms. As such, this earlier data cannot be used for training computer-based reasoning models.

The techniques herein overcome these issues.

The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.

SUMMARY

The claims provide a summary of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 is a flow diagram depicting example processes for feature and case importance and confidence for imputation in computer-based reasoning systems.

FIG. 2 is a block diagram depicting example systems for feature and case importance and confidence for imputation in computer-based reasoning systems.

FIG. 3 is a block diagram of example hardware for feature and case importance and confidence for imputation in computer-based reasoning systems.

FIG. 4 is a flow diagram depicting example processes for controlling systems.

FIG. 5 is a table depicting missing fields for cases in a computer-based reasoning model.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, that the present invention may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to avoid unnecessarily obscuring the present invention.

General Overview

In computer-based reasoning systems, such as case-based reasoning systems, having the appropriate data, and having that data be as complete as possible can be key to success when controlling a system with such a computer-based reasoning system. Numerous types of systems can be controlled with computer-based reasoning systems, such as vehicles, federated device systems, manufacturing equipment, and the like. Numerous examples computer-based reasoning systems being used to control other systems are given throughout herein.

An issue with much training data for computer-based reasoning systems is that the data can be incomplete, which is sometimes known as “sparse” data. Consider, for example, a company that has been drilling oil for many years. As alluded to above, the older pumps may not have had all of the sensors that the current pumps have. Therefore, data from the older pumps may not have all of the “features” or “fields” of data from the newer pumps. Even if the older pumps are retrofitted with newer sensors in order to obtain the same data as newer pumps, the older data from the older pumps will still be missing data for sensors that were not there at the time of collection. Additionally, even if all of the pumps have the same sensors, data can be lost or corrupted, and/or the sensors can fail, each of which causes certain features to be missing from certain cases.

The techniques discussed herein address some of the issues of sparse data being used in computer-based reasoning systems, sometimes referred to as semi-supervised learning. The techniques generally proceed by analyzing the cases in a computer-based reasoning model and determining which cases have missing fields. In some embodiments, conviction scores are then determined for the cases, and the cases and/or the features of the computer-based reasoning model are then ordered by conviction. In some embodiments, conviction score is a broad term encompassing it plain and ordinary meaning, including the certainty (e.g., as a certainty function) that a particular set of data fits a model, the confidence that a particular set of data conforms to the model, or the importance of a feature or case with regard to the model. In some embodiments, convictions scores may be determined based on confidence intervals, frequency of appearance in sets of decision trees, purity of subsections of the model, permutation feature importance, entropy measures (such as cross entropy and Kullback-Leibler (KL) divergence), variance, accuracy when dropping out data, Bayesian posterior probabilities, other scores or tests, such as goodness of fit tests, and/or using one or more of the techniques described herein. This will be discussed in more detail below. The cases with the highest conviction then have the data for the missing fields imputed based on an imputation model. The imputation model may be, for example, a supervised machine learning model that is built based on the existing data in the computer-based reasoning model. The imputed data is added into the individual case and that modified case is added into the computer-based reasoning model to create an updated computer-based reasoning model.

In some embodiments, multiple missing fields are imputed in a batch before the supervised machine learning imputation model is updated and conviction of missing fields is recomputed. In other embodiments, the machine learning imputation model, and the determination of conviction for missing fields is determined after each missing field has been updated with imputed data. After the one or more missing fields have been imputed and the cases and computer-based reasoning model have been updated, the techniques return to determine whether there are any more cases in the computer-based reasoning model for which data needs to be imputed. If there are more cases for which data is missing, then conviction is again determined for those cases and/or the features related to the missing fields. Then the missing data for the cases with high conviction is imputed, and those updated cases are added into the computer-based reasoning model, and the process continues.

Once the computer-based reasoning model has been updated with the imputed data, it will be more complete, or less sparse, than it was previously. The updated computer-based reasoning model can then be used to control a system.

Processes for Feature Importance and Confidence for Imputation in Computer-Based Reasoning Systems

FIG. 1 is a flow diagram depicting example processes for feature and case importance and confidence for imputation in computer-based reasoning systems. FIG. 1 depicts various embodiments of process 100, which proceeds by determining 110 which cases have fields to impute in the computer-based reasoning model. In some embodiments, data needs to be imputed when the data for a field is missing from a case. For example, in FIG. 5, data for features (or “fields”) 511 and 513 are missing from case 522. In some embodiments, conviction scores are determined for the cases with missing data, and, based on the conviction scores, a determination 130 is made as to which cases to impute data. In some embodiments, imputation order information (such as numbers of missing features and/or conviction scores) is determined 120 and the order of imputation is determined 130 based on the imputation order information. The data to impute is determined 140 based on the case with the missing data and an imputation model, such as a machine learning model. The underlying case and the computer-based reasoning model are then modified 150 to include the imputed data. As noted above, multiple missing fields for multiple cases can be computed all at once as part of a batch process, or the techniques can proceed by imputing just a single field's data before returning to determine 110 whether there are more fields to impute in the computer-based reasoning model. If there is more data to impute in the computer-based reasoning model, then the imputation model is updated 170. If there is there is no more data to impute 160, then the updated computer-based reasoning model can be used to control a real-world system. Numerous examples of systems being controlled by the updated computer-based reasoning model are given throughout herein.

Returning again to the top of process 100, a determination 110 is made as to which cases have fields to impute in the computer-based reasoning model. Determining 110 which cases have data to impute can include looking at the underlying data for all of the cases in the computer-based reasoning model. When a field is missing, it can be marked so that a later determination can be made whether to impute data for that missing field. In some embodiments, a separate data structure can be used to store indications of which fields are missing from cases. In some embodiments, the missing data can be indicated directly in the data structure storing the data for the cases. For example, in some embodiments, a graphic processing unit may allow for a special identifier to be used instead of a floating point number in a data structure, such as Null, or NaN or “not a number.” If a data field is missing it may be marked as Null or NaN. FIG. 5 depicts a set of cases 520-523, each with an indication of what the values for each of the features 511-513. As indicated in FIG. 5, case 521 is missing feature 512, so the corresponding field is marked as Null. The same is true for case 522 for features 511 and 513.

Once it is known which cases have fields to impute in the computer-based reasoning model, then, in some embodiments, conviction scores for those cases can be determination 120. In some embodiments, a conviction score is determined 120 for each case with missing data, for all cases in the computer-based reasoning model, etc. Determining 120 a conviction score for a particular case can be accomplished by determining the result of a certainty function for the particular case. Determining case conviction may include determining the confidence intervals, frequency of appearance values in sets of decision trees, purity of subsections of the model where the case would be classified, permutation case importance, Simpson index, entropy measures (such as cross entropy, KL divergence, Shannon entropy, symmetrized divergence, Jensen-Shannon divergence, min entropy, entropy generalizations such as Hartly and Rényi entropy), variance, accuracy when dropping out data, nearest neighbors analysis, Bayesian posterior probabilities, Pearson correlation, mutual information, Gini coefficients, Fisher information, etc. for the case. In some embodiments, not depicted in FIG. 1, influence functions are used to determine the importance of a feature or case.

In some embodiments, determining 120 conviction scores and/or imputation order information can include determining the conviction of each feature of multiple features of the cases in the computer-based reasoning model. In this context the word “feature” is being used to describe a data field as across all or some of the cases in the computer-based reasoning model. The word “field,” in this context, is being used to describe the value of an individual case for a particular feature. For example, a feature for a theoretical computer-based reasoning model for self-driving cars may be “speed”. The field value for a particular case for the feature of speed may be the actual speed, such as thirty-five miles per hour.

Returning to determining 120 conviction scores and/or imputation order information, in some embodiments, determining the conviction of a feature may be accomplished using a certainty function, for example, by determining the feature importance. Determining the feature importance can include determining 120 the statistical significance of each feature in the data with respect to its effect on the generated model, or on a subset of the model. For example, in some embodiments, feature importance is determined 120 by determining the predictor ranking based on the contribution predictors make to the model, where the predictor may be a feature. This technique may be useful in determining whether certain features are contributing too much or little to the model. Feature importance can be determined 120 using numerous techniques. For example, feature importance may be determined 120 using confidence intervals, frequency of appearance in sets of decision trees, purity of subsections of the model, permutation feature importance, entropy measures (such as cross entropy and KL divergence), variance, accuracy when dropping out data, Bayesian posterior probabilities, or Pearson correlation. Returning to the example above, removing a speed feature from a self-driving car computer-based reasoning model could include removing all of the speed values (e.g., fields) from cases from the computer-based reasoning model and determining the conviction of adding speed back into the computer-based reasoning model. In some embodiments, the feature is not actually removed from the database, but only temporarily excluded.

In some embodiments, determining 120 the imputation order information includes determining a sorted order of the cases, sorted by the number of missing features in the case. Determining 130 imputation order, may then include determining 130 the cases with the fewest (or, in some embodiments, most) missing features can then have the missing features imputed first. For example, cases with two missing features may have the missing data imputed before cases with three missing features, and so on. Further, among the cases with the same number of missing features, the cases may again be sorted. For example, all cases with N missing features may be sorted by the conviction associated with the cases themselves and/or missing features as discussed herein (and, for example, the cases with the highest or lowest conviction among the missing features may have their data imputed first).

In some embodiments, after determining 120 the conviction and/or imputation order information of the multiple features of the computer-based reasoning model those features may then be sorted by conviction and/or by the number of missing features. The determination 130 of which one or more cases with missing fields to impute data and/or the order in which to impute data can then be made in any appropriate manner, including, first sorting by the conviction of the features and starting with the feature with the highest conviction (or the two or more features with the highest conviction) and then choosing the cases with the highest conviction among those with missing data for the feature(s) with highest conviction. In some embodiments, the cases are sorted by conviction, without consideration of conviction related to features, and the cases with the highest conviction that have one or more missing fields, have those all or a subset of the missing one or more fields imputed. In some embodiments, the conviction of the feature is multiplied by the conviction of the case (that is missing that feature) and/or the number of missing features. In some embodiments, other equations can be used, such as adding the conviction of the feature to the conviction of the case, adding or multiplying the square (or other exponential power) of one or both convictions, using the maximum of the two convictions, and/or the like, and/or adding to or multiplying any of the former by the number of missing features. The case with the highest product (or other function) of these two conviction numbers and/or the number of missing features is chosen as the next case for which to impute data.

As discussed above, when multiple cases have the same number of features to impute and/or have the same or within a threshold conviction score, then the data for those cases may be imputed in random order (and the model updated after each, as discussed herein), in batches (e.g., impute data for N cases simultaneously), or all at once. In embodiments discussed herein, the imputation order information may first include, as on example, determining that N (>1) cases have the same number of missing features. After that, those N cases may be sorted (e.g., based on conviction as discussed herein). In some cases and embodiments, however, determining 130 which cases to impute data based on the conviction scores and/or the order in which to impute data may include determining a single case with the highest (or lowest) conviction and/or the fewest number of features to impute (e.g., if there is just one case with a single field to impute, that case may have its data imputed first). In this case, the process 100 will proceed by modifying 150 that particular case, updating 170 the imputation model, and then recomputing convictions and or imputation order information.

As discussed herein, in some embodiments, multiple cases may be modified 150 before the imputation model is updated 170 and further cases are determined 110 for imputation. In some embodiments, the balance of the number of cases to impute data before performing updates may be determined based on the computational cost of updating 170 the imputation model and determining 120 the conviction scores. In some embodiments, updating after fewer cases may provide more accurate or robust data imputation using the techniques herein. If computational efficiency is a stronger concern, then more cases may be modified 150 before the imputation model is updated 170 and new conviction scores are determined 120.

After determining 130 which cases to impute data and/or the order in which to impute data, the imputed data is determined 140 based on the case with the missing data and the imputation model. The imputation model may be any appropriate statistical or other machine learning model. For example, a supervised machine learning model may be trained based on the data in the computer-based reasoning model. For example, existing fields (e.g., fields that are not empty) for each feature may be used as the outcome variable and the rest of the features could be used as the input variables. Such a machine learning model would then be able to predict what data is missing for each missing field for each case. Examples of machine learning and other models that may be used may include any appropriate type of supervised neural network or other machine learning method can be used for determining rankings such as a feedforward neural network, a radial basis function neural network, a Kohonen self-organizing neural network, a recurrent neural network, a convolutional neural network, a modular neural network, a k-nearest neighbor approach, and/or the like.

The case and the computer-based reasoning model are then modified 150 based on the imputed data. For example, the case may be updated to copy the imputed data in the missing field (for example, replacing the Null for feature 512 for case 521). The computer-based reasoning model may then be updated to include the updated case, which will replace the previous version of that case with the modified version of that case with the imputed data.

As indicated by the dotted line from modifying 150 to determining 110 (if, for example, a new determination of cases with missing fields is needed) and determining 120 (if, for example, the system still has available the list of cases with missing fields and those cases just need new conviction scores), it may be the case that multiple cases are updated in batch using the same imputation model. Any appropriate batch size can be used, such as two, ten, twenty, one hundred, one thousand cases, etc. In some embodiments, a batch may include a percentage of the cases in the computer-based reasoning system, such as 1%, 2%, 3%. In some embodiments batch size is a combination of a percentage and a fixed number. For example, the batch size may be the larger of 1% of the cases in the computer-based reasoning model or 10 cases. In other embodiments, for example, it may be the lesser of 20 cases or 3% of the computer-based reasoning model. As noted elsewhere herein, larger batches may be useful to improve performance and reduce computational spend. Smaller batches may provide more accurate results. In cases where the data is extremely sparse, smaller batch sized may be useful in order to increase the accuracy of the imputation of data.

Regardless of whether a single case or multiple cases are updated using the imputation model, after those one or more cases are updated and the computer-based reasoning model is modified 150, a determination is made whether there is more data to impute 160 in the computer-based reasoning model. If there is more data to impute 160 then the imputation model is updated 170 before returning control to the top of process 100. Updating 170 the imputation model may include completely retraining the imputation model based on the updated data in the computer-based reasoning model in similar manner to what is described elsewhere herein. In some embodiments, the updated data is added to the imputation model in order to update 170 the model. For example, the updated cases can be used as training data in the supervised machine learning model, in the manner similar to that described here in order to update 170 the supervised machine learning imputation model.

If there are no more cases for which dated needs to be imputed 160 then the updated computer paced reasoning model can be sued to control or cause 199 control of various systems, such as controllable systems. Discussion of the control of numerous systems can be found throughout herein. In some embodiments, process 100 proceeds until there are no more missing fields in the computer-based reasoning model. In some embodiments, the process 100 will terminate earlier. For example, process 100 may terminate when the conviction for the remaining cases fall below a certain threshold. As another example, the process 100 may stop when there remain only a certain threshold number of missing cases (e.g. 100 cases, 1% of the cases, the minimum of 5% or 30 cases, etc.). In some embodiments, process 100 may terminate when the computer-based reasoning model is needed for controlling a system.

In some embodiments, not depicted in FIG. 1, imputation session data is recorded for each imputed field so that audits of explanation scan trace back to the imputation. For example, if an anomaly in the computer-based reasoning model is later detected, a determination can be made whether any of the imputed data was related to the anomaly.

In some embodiments, not depicted in FIG. 1, data may be imputed as new training data is received. For example, each time a new case is received as part of training data for a computer-based reasoning model, any missing data in that training data may be imputed. The missing data may be because of a missing sensor, and/or a malfunctioning sensor.

In some embodiments, not depicted in FIG. 1, previously imputed data may also be removed and reimputed at any appropriate interval or after some criteria have been met. For example, on an hourly, daily, weekly, monthly, yearly, etc. basis, or after a sufficient volume or percentage of volume of additional data has been trained, or after the conviction, feature importance, certainty, or other measure of new or existing cases changed past certain thresholds, data that was previously imputed may be reimputed using the techniques herein. For example, if a series of new cases (e.g., 30 new cases) come in, and their average feature importance is higher (or lower) than the threshold, then the system will reimpute. The associated case and computer-based reasoning model can then be updated appropriately. In some embodiments, this periodic refreshing of the imputed data may provide a benefit if the computer-based reasoning model has been augmented or in any way changed after the data was previously imputed. The deleted and reimputed data may be more accurately imputed based on the changes made to the model in the interim.

Overview of Surprisal, Entropy, and Divergence

Below is a brief summary of some concepts discussed herein. It will be appreciated that there are numerous ways to compute the concepts below, and that other, similar mathematical concepts can be used with the techniques discussed herein.

Entropy (“H(x)”) is a measure of the average expected value of information from an event and is often calculated as the sum over observations of the probability of each observation multiple by the negative log of the probability of the observation. H(x)=−Σ_(i) p(x _(i))*log p(x _(i))

Entropy is generally considered a measure of disorder. Therefore, higher values of entropy represent less regularly ordered information, with random noise having high entropy, and lower values of entropy represent more ordered information, with a long sequence of zeros having low entropy. If log₂ is used, then entropy may be seen as representing the theoretical lower bound on the number of bits needed to represent the information in a set of observations. Entropy can also be seen as how much a new observation distorts a combined probability density or mass function of the observed space. Consider, for example, a universe of observations where there is a certain probability that each of A, B, or C occurs, and a probability that something other than A, B, or C occurs.

Surprisal (“I(x)”) is a measure of how much information is provided by a new event x_(i). I(x _(i))=−log p(x _(i))

Surprisal is generally a measure of surprise (or new information) generated by an event. The smaller the probability of X_(i), the higher the surprisal.

Kullback-Leibler Divergence (“KL divergence” or “Div_(KL)(x)”) is a measure of difference in information between two sets of observation. It is often represented as

-   -   Div_(KL)(x)=Σ_(i)p(x_(i))*(log p(x_(i))−log q(x_(i))), where         p(x_(i)) is the probability of x_(i) after x_(i) has occurred,         and q(x_(i)) is the probability of x_(i) before x_(i) has         occurred.         Conviction Ratios Examples

Returning again to determining 120 conviction scores and/or imputation order information, in some embodiments, the relative surprisal or conviction of a feature within certain scopes, and in comparison to other scopes, can be determined 120. For example, a feature may have high conviction locally (within the near N neighboring cases, as measured by a distance measure such as those described herein), and lower conviction elsewhere, or vice versa. In the former, the feature would be considered locally stable and globally noisy. In the latter, the opposite would hold and it would be locally noisy and globally stable.

Many possible scopes for conviction determination could be used and compared. A few are presented here, and others may also be used. In some embodiments, each scope compared may be a function of the distance from a case. For example, as discussed elsewhere herein a region may be determined. The region may include the N most similar cases to the case in question, the most similar P percent (as compared to the entire model), the cases within distance D, or the cases within a local density distribution, as discussed elsewhere herein. For example, the N most similar cases to the suggested case (or to the input context) may be determined based on a distance measure, such as those described herein. The number N may be a constant, either globally or locally specified, or a relative number, such as a percentage of the total model size. Further, the cases in the region may also be determined based on density. For example, as discussed elsewhere herein, if the cases around the case of interest meet a particular density threshold, those most similar cases could be included in the regional set of cases (and cases not meeting those density thresholds could be excluded). Further, in some embodiments, the similarity (or distance) may be measured based on the context only, the action only, or the context and the action. In some embodiments, only a subset of the context and/or action is used to determine similarity (or distance).

The following are some example measures that may be determined:

-   -   W: Conviction of feature in the whole model;     -   X: Conviction of a feature outside the regional model;     -   Y: Conviction of a feature inside the regional model;     -   Z: Conviction of feature for local (k neighbors) model;     -   where “local” would typically, but not always, constitute a         smaller number of cases than the “regional” model.

As discussed elsewhere herein, conviction can be measured in numerous ways, including excluding a feature from a particular model or portion of a model and measure the conviction as a function the surprisal of putting the feature (or features, or data elements) back in. Conviction measures are discussed extensively herein.

As noted, above, other measures (other than W, X, Y, and Z, listed above) can be used. After two (or more) of the conviction measures are calculated, the ratio of those measures may be determined. For example, in some embodiments, a determined 120 conviction score (ratio) may indicate whether a suggested case or feature of a case is “noisy.” The noisiness of a feature can be determined 140 as a conviction score, in some embodiments, by determining local noisiness and/or relative noisiness. In some embodiments, local noisiness can be determined by looking for the minimum of Y (or looking for the number of cases with Y<1). Relative noisiness may be determined based on the ratio of Z to W. As another example, in some embodiments, a high feature conviction ratio between W and Y may indicate that the feature may be “noisy.” The noisiness of the feature may be indicated based on the ratio of W to Y and/or Y to W.

In some embodiments, measure other that W, X, Y, and Z listed above may include measures based on feature importance to a given target, feature importance to the whole model, predictability of a feature with or without confidence bounds, measures of whether features contribute to or detract from accuracy, and/or the like. For example, in some embodiments, the techniques include determining prediction conviction for features based on a conviction of the accuracy of the prediction using residuals. Using such techniques may be beneficial when features that negatively impact accuracy in a region may be considered “noisy” and therefore be useful as a measure to include in a determination of whether to automatically cause 199 performance of a suggested action.

Once the noisiness of a case/feature is determined 120 as a conviction score and/or imputation data, a decision can later be made whether to cause 199 performance of the suggested action. For example, if the features (or action) of the suggested case are not noisy (locally and/or regionally, depending on the embodiment), then a system may be confident in performing the suggested action in the suggested case. If, however, the features (or action) of the suggested case are noisy, then that noisiness measure may be provided along with the suggested action. Ise, a human operator may then review the noisiness data and determine whether to perform the suggested action, a different action, or no action at all.

Prediction Conviction Examples

Returning again to determining 120 conviction scores and/or imputation order information, in some embodiments, the conviction score is a prediction conviction of a suggested case. As such, determining 120 the certainty score can be determined as the prediction conviction. In some embodiments, when the prediction conviction is determined to be above a certain threshold, then performance of the suggested action can be caused 199. If the prediction conviction is determined to be below a certain threshold, then the prediction conviction score can be provided along with the suggested cases. A human operator may then review the prediction conviction (and any other explanation data) and determine whether to perform the suggested action, a different action, or no action at all.

Determination of prediction conviction is given below. First, familiarity conviction is discussed. Familiarity conviction is sometimes called simply “conviction” herein. Prediction conviction is also sometimes referred to as simply “conviction” herein. In each instance where conviction is used as the term herein, any of the conviction measures may be used. Further, when familiarity conviction or prediction conviction terms are used, those measure are appropriate, as are the other conviction measures discussed herein.

Feature Prediction Contribution Examples

Returning again to determining 120 conviction score and/or imputation order information, in some embodiments, feature prediction contribution is determined 120 as a conviction score and/or imputation order information. Various embodiments of determining 120 feature prediction contribution are given below. In some embodiments, feature prediction contribution can be used to flag what features are contributing most (or above a threshold amount) to a suggestion. Such information can be useful for either ensuring that certain features are not used for particular decision making and/or ensuring that certain features are used in particular decision making. If the feature prediction contribution of a prohibited feature is determined to be above a certain threshold, then the suggested action along with explanation data for the feature prediction contribution can be provided to a human operator, who may then perform the suggested action, a different action, or no action at all. If the feature prediction contribution for undesirable features are determined to be below a certain threshold, then performance of the suggested action may be caused 199 automatically.

Consider unknown and undesirable bias in a computer-based reasoning model. An example of this would be a decision-making computer-based reasoning model planning based on a characteristic that it should not, such deciding whether to approve a loan based on the height of an applicant. The designers, user, or other operators of a loan approval system may have flagged height as a prohibited factor for decision making. If it is determined that height was a factor (for example, the feature prediction contribution is above a certain threshold) in a loan decision, that information can be provided to a human operator, who may then decide to perform the suggested action (approve the loan notwithstanding that it was made at least in part based on height), a different action, or no action at all. If the feature prediction contribution of height is below the certain threshold, then the loan may be approved without further review based on the contribution of height to the decision.

As noted above, in some embodiments, there may also be features whose contribution are desired (e.g., credit score in the case of a loan approval). In such cases, if the feature prediction contribution for a feature whose contribution is desired is determined to be below a certain threshold, then the suggested action along with the feature prediction contribution may be provided to a human operator who may then decide to perform the suggested action (approve the loan notwithstanding that it was made at without contribution of the desired feature), a different action, or no action at all. If the feature prediction contribution of the desired feature is below the above threshold, then performance of the action may be caused 199 (e.g., loan may be approved) without further review based on the contribution of the desired feature (e.g., credit score) to the decision.

In some embodiments, not depicted in FIG. 1, the feature contribution is used to reduce the size of a model in a computer-based reasoning system. For example, if a feature does not contribute much to a model, then it may be removed from the model. As a more specific example, the feature prediction contribution may be determined for multiple input contexts (e.g., tens of, hundreds of, thousands of, or more) input contexts and the feature contribution may be determined for each feature for each input context. Those features that never reach an exclusionary threshold amount of contribution to a decision (e.g., as determined by the feature prediction contribution) may be excluded from the computer-based reasoning model. In some embodiments, only those features that reach an inclusion threshold may be included in the computer-based reasoning model. In some embodiments, both an exclusionary lower threshold and inclusionary upper threshold may be used. In other embodiments, average contribution of a feature may be used to rank features and the top N features may be those included in the models. Excluding features from the model may be beneficial in embodiments where the size of the model causes the need for extra storage and/or computing power. In many computer-based reasoning systems, smaller models (e.g., with fewer features being analyzed) may be more efficient to store and when making decision. The reduced models may be used, for example, with any of the techniques described herein.

Familiarity Conviction Examples

In some embodiments, the conviction score and/or imputation order information determined 120 is familiarity conviction in order to measure of how much information the point distorts the model. To do so, the techniques herein may define a feature information measure, such as familiarity conviction such that a point's weighted distance contribution affects other points' distance contribution and compared to the expected distance contribution of adding any new point.

Definition 1. Given a point x∈X and the set K of its k nearest neighbors, a distance function d: R^(z)×Z→R, and a distance exponent α, the distance contribution of x may be the harmonic mean

$\begin{matrix} {{\phi(x)} = {\left( {\frac{1}{K}{\sum\limits_{k \in K}\frac{1}{{d\left( {x,k} \right)}^{\alpha}}}} \right)^{- 1}.}} & (1) \end{matrix}$

Definition 2. Given a set of points X⊂R^(z) for every x∈X and an integer 1≤k<|X| one may define the distance contribution probability distribution, C of X to be the set

$\begin{matrix} {C = \left\{ {\frac{\phi\left( x_{1} \right)}{\sum\limits_{i = 1}^{n}{\phi\left( x_{i} \right)}},\ \frac{\phi\left( x_{2} \right)}{\sum\limits_{i = 1}^{n}{\phi\left( x_{i} \right)}},\ldots\mspace{14mu},\frac{\phi\left( x_{n} \right)}{\sum\limits_{i = 1}^{n}{\phi\left( x_{i} \right)}}} \right\}} & (2) \end{matrix}$ for a function φ: X→R that returns the distance contribution.

Note that if φ(0)=∞, special consideration may be given to multiple identical points, such as splitting the distance contribution among those points.

Remark 1. C may be a valid probability distribution. In some embodiments, this fact is used to compute the amount of information in C.

Definition 3. The point probability of a point x_(i), i=1, 2, . . . , n may be

$\begin{matrix} {{l(i)} = \frac{\phi\left( x_{i} \right)}{\sum\limits_{i}^{\;}{\phi\left( x_{i} \right)}}} & (3) \end{matrix}$ where the index i is assigned the probability of the indexed point's distance contribution. One may denote this random variable L.

Remark 2. When points are selected uniformly at random, one may assume L is uniform when the distance probabilities have no trend or correlation.

Definition 4. The conviction of a point x_(i)∈X may be

$\begin{matrix} {{\pi\left( x_{i} \right)} = \frac{\frac{1}{X}{\sum\limits_{i}^{\;}{{\mathbb{K}\mathbb{L}}\left( {{{L{}L} - \left\{ i \right\}}\bigcup{{\mathbb{E}}\;{l(i)}}} \right)}}}{{\mathbb{K}\mathbb{L}}\left( {{{L{}L} - \left\{ x_{i} \right\}}\bigcup{{\mathbb{E}}\;{l(i)}}} \right)}} & (4) \end{matrix}$ where KL is the Kullback-Leibler divergence. In some embodiments, when one assumes L is uniform, one may have that the expected probability:

${\mathbb{E}}\;{l(i)}{= {\frac{1}{n}.}}$ Prediction Conviction Examples

In some embodiments, the conviction score and/or imputation order information determined 120 is prediction conviction as a proxy for accuracy of a prediction. Techniques herein may determine prediction conviction such that a point's weighted distance to other points is of primary importance, and can be expressed as the information required to describe the position of the point in question relative to existing points.

Definition 5. Let ξ be the number of features in a model and n the number of observations. One may define the residual function of the training data X: r: X→R ^(ξ) r(x)=J ₁(k,p),J ₂(k,p), . . . ,J _(ξ)(k,p)  (5)

Where J_(i) may be the residual of the model on feature i parameterized by the hyperparameters k and p evaluated on points near x. In some embodiments, one may refer to the residual function evaluated on all of the model data as r_(M). In some embodiments, the feature residuals may be calculated as mean absolute error or standard deviation.

In some embodiments, one can quantify the information needed to express a distance contribution φ(x) by moving to a probability. In some embodiments, the exponential distribution may be selected to describe the distribution of residuals, as it may be the maximum entropy distribution constrained by the first moment. In some embodiments, a different distribution may be used for the residuals, such as the Laplace, log normal distribution, Gaussian distribution, normal distribution, etc.

The exponential distribution may be represented or expressed as:

$\begin{matrix} {\frac{1}{\lambda} = {{r(x)}}_{p}} & (6) \end{matrix}$

We can directly compare the distance contribution and p-normed magnitude of the residual. This is because the distance contribution is a locally weighted expected value of the distance from one point to its nearest neighbors, and the residual is an expected distance between a point and the nearest neighbors that are part of the model. Given the entropy maximizing assumption of the exponential distribution of the distances, we can then determine the probability that a distance contribution is greater than or equal to the magnitude of the residual ∥r(x)∥_(p) as:

$\begin{matrix} {{P\left( {{\varphi(x)} \geq {{r(x)}}_{p}} \right)} = {e^{{- \frac{1}{{{r{(x)}}}_{p}}} \cdot {\varphi{(x)}}}.}} & (7) \end{matrix}$

We then convert the probability to self-information as: I(x)=−ln P(φ(x)≥∥r(x)∥_(p)),  (8) which simplifies to:

$\begin{matrix} {{I(x)} = {\frac{\varphi(x)}{{{r(x)}}_{p}}.}} & (9) \end{matrix}$

As the distance contribution decreases, or as the residual vector magnitude increases, the less information may be needed to represent this point. One can then compare this to the expected value a regular conviction form, yielding a prediction conviction of:

$\begin{matrix} {{\pi_{p} = \frac{EI}{I(x)}},} & (10) \end{matrix}$ where I is the self-information calculated for each point in the model. Additional Prediction Conviction Examples

In some embodiments, φ(x) may be the distance contribution of point x, and r(x) may be the magnitude of the expected feature residuals at point x using the same norm and same topological parameters as the distance contribution, putting both on the same scale.

The probability of both being less than the expected values may be: P(ϕ(x)>

ϕ(x))·P(r(x)>

r(x)).

The self-information of this, which may be the negative log of the probability

$\begin{matrix} {I = {\frac{\phi(x)}{{\mathbb{E}\phi}(x)} + {\frac{r(x)}{{\mathbb{E}}\;{r(x)}}.}}} & \; \end{matrix}$

The prediction conviction

$\pi_{p} = \frac{{\mathbb{E}}\; I}{I}$ may then be calculated, which simplifies to:

$\pi_{p} = {\frac{2}{\frac{\phi(x)}{{\mathbb{E}\phi}(x)} + \frac{r(x)}{{\mathbb{E}}\;{r(x)}}}.}$ Feature Prediction Contribution Examples

In some embodiments, the conviction score and/or imputation order information determined 120 is feature prediction contribution, which may be related Mean Decrease in Accuracy (MDA). In MDA scores are established for models with all the features M and models with each feature held out M−_(fi),i=1 . . . ξ. The difference |M−M−_(fi)| is the importance of each feature, where the result's sign is altered depending on whether the goal is to maximize or minimize score.

In some embodiments, prediction information π_(c) is correlated with accuracy and thus may be used as a surrogate. The expected self-information required to express a feature is given by:

${{E{I(M)}} = {\frac{1}{\xi}{\sum\limits_{i}^{\xi}{I\left( x_{i} \right)}}}},$ and the expected self-information to express a feature without feature i is

${E{I\left( M_{- i} \right)}} = {\frac{1}{\xi}{\sum\limits_{j = 0}^{\xi}{{I_{- i}\left( x_{j} \right)}.}}}$

One can now make two definitions:

Definition 6. The prediction contribution π_(c) of feature i is

${\pi_{c}(i)} = {\frac{M - M_{- f_{i}}}{M}.}$

Definition 7. The prediction conviction, pi_(p), of feature i is

${\pi_{p}(i)} = {\frac{\frac{1}{\xi}{\sum\limits_{i = 0}^{\xi}M_{- f_{i}}}}{M_{- f_{i}}}.}$ Targeted and Untargeted Techniques for Determining Conviction and Other Measures

In some embodiments, any of the feature information measures, conviction or contribution measures (e.g., surprisal, prediction conviction, familiarity conviction, and/or feature prediction contribution and/or feature prediction conviction) may be determined using an “untargeted” and/or a “targeted” approach. In the untargeted approach, the measure (e.g., a conviction measure) is determined by holding out the item in question and then measuring information gain associated with putting the item back into the model. Various examples of this are discussed herein. For example, to measure the untargeted conviction of a case (or feature), the conviction is measured in part based on taking the case (or feature) out of the model, and then measuring the information associated with adding the case (or feature) back into the model.

In order to determine a targeted measure, such as surprisal, conviction, or contribution of a data element (e.g., a case or a feature), in contrast to untargeted measures, everything is dropped from the model except the features or cases being analyzed (the “analyzed data element(s)”) and the target features or cases (“target data element(s)”). Then the measure is calculated by measure the conviction, information gain, contribution, etc. based on how well the analyzed data element(s) predict the target data element(s) in the absence of the rest of the model.

In each instance that a measure, such as a surprisal, conviction, contribution, etc. measure, is discussed herein, the measure may be determined using either a targeted approach or an untargeted approach. For example, when the term “conviction” is used, it may refer to targeted or untargeted prediction conviction, targeted or untargeted familiarity conviction, and/or targeted or untargeted feature prediction conviction. Similarly, when surprisal, information, and/or contribution measures are discussed without reference to either targeted or untargeted calculation techniques, then reference may be being made to either a targeted or untargeted calculation for the measure.

Systems for Feature and Case Importance and Confidence for Imputation in Computer-Based Reasoning Systems

FIG. 2 is a block diagram depicting example systems for conviction-based imputation and computer-based reasoning systems. Numerous devices and systems are coupled to a network 290. Network 290 can include the internet, a wide area network, a local area network, a Wi-Fi network, any other network or communication device described herein, and the like. Further, numerous of the systems and devices connected to 290 may have encrypted communication there between, VPNs, and or any other appropriate communication or security measure. System 200 includes a training and analysis system 210 coupled to network 290. The training and analysis system 210 may be used for collecting data related to systems 250-258 and creating computer-based reasoning models based on the training of those systems. Further, training and analysis system 210 may perform aspects of process 100 described herein. Control system 220 is also coupled to network 290. A control system 220 may control various of the systems 250-258. For example, a vehicle control 221 may control any of the vehicles 250-253, or the like. In some embodiments, there may be one or more network attached storages 230, 240. These storages 230, 240 may store training data, computer-based reasoning models, updated computer-based reasoning models, audit trails of imputed data, and the like. In some embodiments, training and analysis system 210 and/or control system 220 may store any needed data including computer-based reasoning models locally on the system.

FIG. 2 depicts numerous systems 250-258 that may be controlled by a control system 220 or 221. For example, automobile 250, helicopter 251, submarine 252, boat 253, factory equipment 254, construction equipment 255, security equipment 256, oil pump 257, or warehouse equipment 258 may be controlled by a control system 220 or 221.

Example Processes for Controlling Systems

FIG. 4 depicts an example process 400 for controlling a system. In some embodiments and at a high level, the process 400 proceeds by receiving or receiving 410 a computer-based reasoning model for controlling the system. The computer-based reasoning model may be one created using process 100, as one example. In some embodiments, the process 400 proceeds by receiving 420 a current context for the system, determining 430 an action to take based on the current context and the computer-based reasoning model, and causing 440 performance of the determined action (e.g., labelling an image, causing a vehicle to perform the turn, lane change, waypoint navigation, etc.). If operation of the system continues 450, then the process returns to receive 420 the current context, and otherwise discontinues 460 control of the system. In some embodiments, causing 199 performance of a selected action may include causing 440 performance of a determined action (or vice-versa).

As discussed herein the various processes 100, 400, etc. may run in parallel, in conjunction, together, or one process may be a subprocess of another. Further, any of the processes may run on the systems or hardware discussed herein. The features and steps of processes 100 and 400 could be used in combination and/or in different orders.

Self-Driving Vehicles

Returning to the top of the process 400, it begins by receiving 410 a computer-based reasoning model for controlling the system. The computer-based reasoning model may be received in any appropriate matter. It may be provided via a network 290, placed in a shared or accessible memory on either the training and analysis system 210 or control system 220, or in accessible storage, such as storage 230 or 240.

In some embodiments (not depicted in FIG. 4), an operational situation could be indicated for the system. The operational situation is related to context, but may be considered a higher level, and may not change (or change less frequently) during operation of the system. For example, in the context of control of a vehicle, the operational situation may be indicated by a passenger or operator of the vehicle, by a configuration file, a setting, and/or the like. For example, a passenger Alicia may select “drive like Alicia” in order to have the vehicle driver like her. As another example, a fleet of helicopters may have a configuration file set to operate like Bob. In some embodiments, the operational situation may be detected. For example, the vehicle may detect that it is operating in a particular location (area, city, region, state, or country), time of day, weather condition, etc. and the vehicle may be indicated to drive in a manner appropriate for that operational situation.

The operational situation, whether detected, indicated by passenger, etc., may be changed during operation of the vehicle. For example, a passenger may first indicate that she would like the vehicle to drive cautiously (e.g., like Alicia), and then realize that she is running later and switch to a faster operation mode (e.g., like Carole). The operational situation may also change based on detection. For example, if a vehicle is operating under an operational situation for a particular portion of road, and detects that it has left that portion of road, it may automatically switch to an operational situation appropriate for its location (e.g., for that city), may revert to a default operation (e.g., a baseline program that operates the vehicle) or operational situation (e.g., the last used). In some embodiments, if the vehicle detects that it needs to change operational situations, it may prompt a passenger or operator to choose a new operational situation.

In some embodiments, the computer-based reasoning model is received before process 400 begins (not depicted in FIG. 4), and the process begins by receiving 420 the current context. For example, the computer-based reasoning model may already be loaded into a controller 220 and the process 400 begins by receiving 420 the current context for the system being controlled. In some embodiments, referring to FIG. 2, the current context for a system to be controlled (not depicted in FIG. 2) may be sent to control system 220 and control system 220 may receive 420 current context for the system.

Receiving 420 current context may include receiving the context data needed for a determination to be made using the computer-based reasoning model. For example, turning to the vehicular example, receiving 420 the current context may, in various embodiments, include receiving information from sensors on or near the vehicle, determining information based on location or other sensor information, accessing data about the vehicle or location, etc. For example, the vehicle may have numerous sensors related to the vehicle and its operation, such as one or more of each of the following: speed sensors, tire pressure monitors, fuel gauges, compasses, global positioning systems (GPS), RADARs, LiDARs, cameras, barometers, thermal sensors, accelerometers, strain gauges, noise/sound measurement systems, etc. Current context may also include information determined based on sensor data. For example, the time to impact with the closest object may be determined based on distance calculations from RADAR or LiDAR data, and/or may be determined based on depth-from-stereo information from cameras on the vehicle. Context may include characteristics of the sensors, such as the distance a RADAR or LiDAR is capable of detecting resolution and focal length of the cameras, etc. Context may include information about the vehicle not from a sensor. For example, the weight of the vehicle, acceleration, deceleration, and turning or maneuverability information may be known for the vehicle and may be part of the context information. Additionally, context may include information about the location, including road condition, wind direction and strength, weather, visibility, traffic data, road layout, etc.

Referring back to the example of vehicle control rules for Bob flying a helicopter, the context data for a later flight of the helicopter using the vehicle control rules based on Bob's operation of the helicopter may include fuel remaining, distance that fuel can allow the helicopter to travel, location including elevation, wind speed and direction, visibility, location and type of sensors as well as the sensor data, time to impact with the N closest objects, maneuverability and speed control information, etc. Returning to the stop sign example, whether using vehicle control rules based on Alicia or Carole, the context may include LiDAR, RADAR, camera and other sensor data, location information, weight of the vehicle, road condition and weather information, braking information for the vehicle, etc.

The control system then determined 430 an action to take based on the current context and the computer-based reasoning model. For example, turning to the vehicular example, an action to take is determined 430 based on the current context and the vehicle control rules for the current operational situation. In some embodiments that use machine learning, the vehicle control rules may be in the form of a neural network (as described elsewhere herein), and the context may be fed into the neural network to determine an action to take. In embodiments using case-based reasoning, the set of context-action pairs closest (or most similar) to the current context may be determined. In some embodiments, only the closest context-action pair is determined, and the action associated with that context-action pair is the determined 430 action. In some embodiments, multiple context-action pairs are determined 430. For example, the N “closest” context-action pairs may be determined 430, and either as part of the determining 430, or later as part of the causing 440 performance of the action, choices may be made on the action to take based on the N closest context-action pairs, where “distance” for between the current context can be measured using any appropriate technique, including use of Euclidean distance, Minkowski distance, Damerau-Levenshtein distance, Kullback-Leibler divergence, and/or any other distance measure, metric, pseudometric, premetric, index, or the like.

In some embodiments, the actions to be taken may be blended based on the action of each context-action pair, with invalid (e.g., impossible or dangerous) outcomes being discarded. A choice can also be made among the N context-action pairs chosen based on criteria such as choosing to use the same or different operator context-action pair from the last determined action. For example, in an embodiment where there are context-action pair sets from multiple operators in the vehicle control rules, the choice of which context-action pair may be based on whether a context-action pair from the same operator was just chosen (e.g., to maintain consistency). The choice among the top N context-action pairs may also be made by choosing at random, mixing portions of the actions together, choosing based on a voting mechanism, etc.

Some embodiments include detecting gaps in the training data and/or vehicle control rules and indicating those during operation of the vehicle (for example, via prompt and/or spoken or graphical user interface) or offline (for example, in a report, on a graphical display, etc.) to indicate what additional training is needed (not depicted in FIG. 4). In some embodiments, when the computer-based reasoning system does not find context “close enough” to the current context to make a confident decision on an action to take, it may indicate this and suggest that an operator might take manual control of the vehicle, and that operation of the vehicle may provide additional context and action data for the computer-based reasoning system. Additionally, in some embodiments, an operator may indicate to a vehicle that she would like to take manual control to either override the computer-based reasoning system or replace the training data. These two scenarios may differ by whether the data (for example, context-action pairs) for the operational scenario are ignored for this time period, or whether they are replaced.

In some embodiments, the operational situation may be chosen based on a confidence measure indicating confidence in candidate actions to take from two (or more) different sets of control rules (not depicted in FIG. 4). Consider a first operational situation associated with a first set of vehicle control rules (e.g., with significant training from Alicia driving on highways) and a second operational situation associated with a second set of vehicle control rules (e.g., with significant training from Carole driving on rural roads). Candidate actions and associated confidences may be determined for each of the sets of vehicle control rules based on the context. The determined 430 action to take may then be selected as the action associated with the higher confidence level. For example, when the vehicle is driving on the highway, the actions from the vehicle control rules associated with Alicia may have a higher confidence, and therefore be chosen. When the vehicle is on rural roads, the actions from the vehicle control rules associated with Carole may have higher confidence and therefore be chosen. Relatedly, in some embodiments, a set of vehicle control rules may be hierarchical, and actions to take may be propagated from lower levels in the hierarchy to high levels, and the choice among actions to take propagated from the lower levels may be made on confidence associated with each of those chosen actions. The confidence can be based on any appropriate confidence calculation including, in some embodiments, determining how much “extra information” in the vehicle control rules is associated with that action in that context.

In some embodiments, there may be a background or baseline operational program that is used when the computer-based reasoning system does not have sufficient data to make a decision on what action to take (not depicted in FIG. 4). For example, if in a set of vehicle control rules, there is no matching context or there is not a matching context that is close enough to the current context, then the background program may be used. If none of the training data from Alicia included what to do when crossing railroad tracks, and railroad tracks are encountered in later operation of the vehicle, then the system may fall back on the baseline operational program to handle the traversal of the railroad tracks. In some embodiments, the baseline model is a computer-based reasoning system, in which case context-action pairs from the baseline model may be removed when new training data is added. In some embodiments, the baseline model is an executive driving engine which takes over control of the vehicle operation when there are no matching contexts in the vehicle control rules (e.g., in the case of a context-based reasoning system, there might be no context-action pairs that are sufficiently “close”).

In some embodiments, determining 430 an action to take based on the context can include determining whether vehicle maintenance is needed. As described elsewhere herein, the context may include wear and/or timing related to components of the vehicle, and a message related to maintenance may be determined based on the wear or timing. The message may indicate that maintenance may be needed or recommended (e.g., because preventative maintenance is often performed in the timing or wear context, because issues have been reported or detected with components in the timing or wear context, etc.). The message may be sent to or displayed for a vehicle operator (such as a fleet management service) and/or a passenger. For example, in the context of an automobile with sixty thousand miles, the message sent to a fleet maintenance system may include an indication that a timing belt may need to be replaced in order to avoid a P percent chance that the belt will break in the next five thousand miles (where the predictive information may be based on previously-collected context and action data, as described elsewhere herein). When the automobile reaches ninety thousand miles and assuming the belt has not been changed, the message may include that the chance that the belt will break has increased to, e.g., P*4 in the next five thousand miles.

Performance of the determined 430 action is then caused 440. Turning to the vehicular example, causing 440 performance of the action may include direct control of the vehicle and/or sending a message to a system, device, or interface that can control the vehicle. The action sent to control the vehicle may also be translated before it is used to control the vehicle. For example, the action determined 430 may be to navigate to a particular waypoint. In such an embodiment, causing 440 performance of the action may include sending the waypoint to a navigation system, and the navigation system may then, in turn, control the vehicle on a finer-grained level. In other embodiments, the determined 430 action may be to switch lanes, and that instruction may be sent to a control system that would enable the car to change the lane as directed. In yet other embodiments, the action determined 430 may be lower-level (e.g., accelerate or decelerate, turn 4° to the left, etc.), and causing 440 performance of the action may include sending the action to be performed to a control of the vehicle, or controlling the vehicle directly. In some embodiments, causing 440 performance of the action includes sending one or more messages for interpretation and/or display. In some embodiments, the causing 440 the action includes indicating the action to be taken at one or more levels of a control hierarchy for a vehicle. Examples of control hierarchies are given elsewhere herein.

Some embodiments include detecting anomalous actions taken or caused 440 to be taken. These anomalous actions may be signaled by an operator or passenger, or may be detected after operation of the vehicle (e.g., by reviewing log files, external reports, etc.). For example, a passenger of a vehicle may indicate that an undesirable maneuver was made by the vehicle (e.g., turning left from the right lane of a 2-lane road) or log files may be reviewed if the vehicle was in an accident. Once the anomaly is detected, the portion of the vehicle control rules (e.g., context-action pair(s)) related to the anomalous action can be determined. If it is determined that the context-action pair(s) are responsible for the anomalous action, then those context-action pairs can be removed or replaced using the techniques herein.

Referring to the example of the helicopter fleet and the vehicle control rules associated with Bob, the vehicle control 220 may determine 430 what action to take for the helicopter based on the received 420 context. The vehicle control 220 may then cause the helicopter to perform the determined action, for example, by sending instructions related to the action to the appropriate controls in the helicopter. In the driving example, the vehicle control 220 may determine 430 what action to take based on the context of vehicle. The vehicle control may then cause 440 performance of the determined 430 action by the automobile by sending instructions to control elements on the vehicle.

If there are more 450 contexts for which to determine actions for the operation of the system, then the process 400 returns to receive 410 more current contexts. Otherwise, process 400 ceases 460 control of the system. Turning to the vehicular example, as long as there is a continuation of operation of the vehicle using the vehicle control rules, the process 400 returns to receive 420 the subsequent current context for the vehicle. If the operational situation changes (e.g., the automobile is no longer on the stretch of road associated with the operational situation, a passenger indicates a new operational situation, etc.), then the process returns to determine the new operational situation. If the vehicle is no longer operating under vehicle control rules (e.g., it arrived at its destination, a passenger took over manual control, etc.), then the process 400 will discontinue 460 autonomous control of the vehicle.

Many of the examples discussed herein for vehicles discuss self-driving automobiles. As depicted in FIG. 2, numerous types of vehicles can be controlled. For example, a helicopter 251 or drone, a submarine 252, or boat or freight ship 253, or any other type of vehicle such as plane or drone (not depicted in FIG. 2), construction equipment, (not depicted in FIG. 2), and/or the like. In each case, the computer-based reasoning model may differ, including using different features, using different techniques described herein, etc. Further, the context of each type of vehicle may differ. Flying vehicles may need context data such as weight, lift, drag, fuel remaining, distance remaining given fuel, windspeed, visibility, etc. Floating vehicles, such as boats, freight vessels, submarines, and the like may have context data such as buoyancy, drag, propulsion capabilities, speed of currents, a measure of the choppiness of the water, fuel remaining, distance capability remaining given fuel, and the like. Manufacturing and other equipment may have as context width of area traversing, turn radius of the vehicle, speed capabilities, towing/lifting capabilities, and the like.

Image Labelling

The techniques herein may also be applied in the context of an image-labeling system. For example, numerous experts may label images (e.g., identifying features of or elements within those images). For example, the human experts may identify cancerous masses on x-rays. Having these experts label all input images is incredibly time consuming to do on an ongoing basis, in addition to being expensive (paying the experts). The techniques herein may be used to train an image-labeling computer-based reasoning model based on previously-trained images. Once the image-labeling computer-based reasoning system has been built, then input images may be analyzed using the image-based reasoning system. In order to build the image-labeling computer-based reasoning system, images may be labeled by experts and used as training data. Using the techniques herein, the surprisal of the training data can be used to build an image-labeling computer-based reasoning system that balances the size of the computer-based reasoning model with the information that each additional image (or set of images) with associated labels provides. Once the image-labelling computer-based reasoning is trained, it can be used to label images in the future. For example, a new image may come in, the image-labelling computer-based reasoning may determine one or more labels for the image, and then the one or more labels may then be applied to the image. Thus, these images can be labeled automatically, saving the time and expense related to having experts label the images.

In some embodiments, processes 100 or 400 may include determining the surprisal and/or conviction of each image (or multiple images) and the associated labels or of the aspects of the computer-based reasoning model. The surprisal and/or conviction for the one or more images may be determined and a determination may be made whether to select or include the one or more images (or aspects) in the image-labeling computer-based reasoning model based on the determined surprisal and/or conviction. While there are more sets of one or more images with labels to assess, the process may return to determine whether more image or label sets should be included or whether aspects should be included and/or changed in the model. Once there are no more images or aspects to consider, the process can turn to controlling or causing control of the image analysis system using the image-labeling computer-based reasoning model.

In some embodiments, the techniques include performing the following until there are no more cases in a computer-based reasoning model with missing fields for which imputation is desired: determining 110 which images have fields to impute (e.g., missing fields, e.g., in the metadata of the image) in the computer-based reasoning model and determining 120 conviction scores and/or imputation order information for the images that have fields to impute. The techniques proceed by determining 130 for which images to impute data and/or in what order to impute data based on the conviction scores and/or imputation order information. For each of the determined one or more images with missing fields to impute data is imputed for the missing field, and the image is modified with the imputed data (e.g., adding the imputed metadata to the image file). Once there are no more images or aspects for which to impute data, the process 100 can turn to controlling or causing 199 control of the image analysis system using the image-labeling computer-based reasoning model.

Controlling or causing 199 control of an image-labeling system may be accomplished by process 400. For example, if the data elements are related to images and labels applied to those images, then the image-labeling computer-based reasoning model trained on that data will apply labels to incoming images. Process 400 proceeds by receiving 410 an image-labeling computer-based reasoning model. The process proceeds by receiving 420 an image for labeling. The image-labeling computer-based reasoning model is then used to determine 430 labels for the input image. The image is then labeled 440. If there are more 450 images to label, then the system returns to receive 410 those images and otherwise ceases 460. In such embodiments, the image-labeling computer-based reasoning model may be used to select labels based on which training image is “closest” (or most similar) to the incoming image. The label(s) associated with that image will then be selected to apply to the incoming image.

Manufacturing and Assembly

The processes 100 and/or 400 may also be applied in the context of manufacturing and/or assembly. For example, conviction can be used to identify normal behavior versus anomalous behavior of such equipment. Using the techniques herein, a crane (e.g., crane 255 of FIG. 2), robot arm, or other actuator is attempting to “grab” something and its surprisal is too high, it can stop, sound an alarm, shutdown certain areas of the facility, and/or request for human assistance. Anomalous behavior that is detected via conviction among sensors and actuators can be used to detect when there is some sort breakdown, unusual wear and tear or mechanical or other malfunction, an unusual component or seed or crop, etc. It can also be used to find damaged equipment for repairs or buffing or other improvements for any robots or other machines that are searching and correcting defects in products or themselves (e.g., fixing a broken wire or smoothing out cuts made to the ends of a manufactured artifact made via an extrusion process). Conviction can also be used for cranes and other grabbing devices to find which cargo or items are closest matches to what is needed. Conviction can be used to drastically reduce the amount of time to train a robot to perform a new task for a new product or custom order, because the robot will indicate the aspects of the process it does not understand and direct training towards those areas and away from things it has already learned. Combining this with stopping ongoing actions when an anomalous situation is detected would also allow a robot to begin performing work before it is fully done training, the same way that a human apprentice may help out someone experienced while the apprentice is learning the job. Conviction can also inform what features or inputs to the robot are useful and which are not.

As an additional example in the manufacturing or assembly context, vibration data can be used to diagnose (or predict) issues with equipment. In some embodiments, the training data for the computer-based reasoning system would be vibration data (e.g., the output of one or more piezo vibration sensors attached to one or more pieces of manufacturing equipment) for a piece of equipment along with diagnosis of an issue or error that occurred with the equipment. The training data may similarly include vibration data for the manufacturing equipment that is not associated with an issue or error with the equipment. In subsequent operation of the same or similar equipment, the vibration data can be collected, and the computer-based reasoning model can be used to assess that vibration data to either diagnose or predict potential issues or errors with the equipment. For example, the vibration data for current (or recent) operation of one or more pieces of equipment, the computer-based reasoning model may be used to predict, diagnose, or otherwise determine issues or errors with the equipment. As a more specific example, a current context of vibration data for one or more pieces of manufacturing equipment may result in a diagnosis or prediction of various conditions, including, but not limited to: looseness of a piece of equipment (e.g., a loose screw), an imbalance on a rotating element (e.g., grime collected on a rotating wheel), misalignment or shaft runout (e.g., machine shafts may be out of alignment or not parallel), wear (e.g., ball or roller bearings, drive belts or gears become worn, they might cause vibration). As a further example, misalignment can be caused during assembly or develop over time, due to thermal expansion, components shifting or improper reassembly after maintenance. When a roller or ball bearing becomes pitted, for instance, the rollers or ball bearing will cause a vibration each time there is contact at the damaged area. A gear tooth that is heavily chipped or worn, or a drive belt that is breaking down, can also produce vibration. Diagnosis or prediction of the issue or error can be made based on the current or recent vibration data, and a computer-based reasoning model training data from the previous vibration data and associated issues or errors. Diagnosing or predicting the issues of vibration can be especially important where the vibration can cause other issues. For example, wear on a bearing may cause a vibration that then loosens another piece of equipment, which then can cause other issues and damage to equipment, failure of equipment, and even failure of the assembly or manufacturing process.

In some embodiments, techniques herein may determine (e.g., in response to a request) the surprisal and/or conviction of one or more data elements (e.g., of the manufacturing equipment) or aspects (e.g., features of context-action pairs or aspects of the model) to potentially include in the manufacturing control computer-based reasoning model. The surprisal and/or conviction for the one or more manufacturing elements may be determined and a determination may be made whether to select or include the one or more manufacturing data elements or aspects in the manufacturing control computer-based reasoning model based on the determined surprisal and/or conviction. While there are more sets of one or more manufacturing data elements or aspects to assess (e.g., from additional equipment and/or from subsequent time periods), the process may return to determine whether more manufacturing data elements or aspects sets should be included in the computer-based reasoning model. Once there are no more manufacturing data elements or aspects to consider for inclusion, the process can turn to controlling the manufacturing system using the manufacturing control computer-based reasoning system.

In some embodiments, the techniques include performing the following until there are no more cases in a computer-based reasoning model with missing fields for which imputation is desired: determining 110 which cases have fields to impute (e.g., missing fields in the manufacturing data elements) in the computer-based reasoning model and determining 120 conviction scores and/or imputation order information for the cases that have fields to impute. The techniques proceed by determining 130 for which cases to impute data and/or in what order to impute data based on the conviction scores and/or imputation order information. For each of the determined one or more cases with missing fields to impute data is imputed for the missing field, and the case is modified with the imputed data (e.g., adding the imputed metadata to the case file). Once there are no more cases or aspects for which to impute data, the process 100 can turn to controlling or causing 199 control of the manufacturing analysis system using the manufacturing-control computer-based reasoning model.

Controlling or causing 199 control of a manufacturing system may be accomplished by process 400. For example, if the data elements are related to manufacturing data elements or aspects, then the manufacturing control computer-based reasoning model trained on that data will control manufacturing or assemble. Process 400 proceeds by receiving 410 a manufacturing control computer-based reasoning model. The process proceeds by receiving 420 a context. The manufacturing control computer-based reasoning model is then used to determine 430 an action to take. The action is then performed by the control system (e.g., caused by the manufacturing control computer-based reasoning system). If there are more 450 contexts to consider, then the system returns to receive 410 those contexts and otherwise ceases 460. In such embodiments, the manufacturing control computer-based reasoning model may be used to control a manufacturing system. The chosen actions are then performed by a control system.

Smart Voice Control

The processes 100 and/or 400 may also be applied in the context of smart voice control. For example, combining multiple inputs and forms of analysis, the techniques herein can recognize if there is something unusual about a voice control request. For example, if a request is to purchase a high-priced item or unlock a door, but the calendar and synchronized devices indicate that the family is out of town, it could send a request to the person's phone before confirming the order or action; it could be that an intruder has recorded someone's voice in the family or has used artificial intelligence software to create a message and has broken in. It can detect other anomalies for security or for devices activating at unusual times, possibly indicating some mechanical failure, electronics failure, or someone in the house using things abnormally (e.g., a child frequently leaving the refrigerator door open for long durations). Combined with other natural language processing techniques beyond sentiment analysis, such as vocal distress, a smart voice device can recognize that something is different and ask, improving the person's experience and improving the seamlessness of the device into the person's life, perhaps playing music, adjusting lighting, or HVAC, or other controls. The level of confidence provided by conviction can also be used to train a smart voice device more quickly as it can ask questions about aspects of its use that it has the least knowledge about. For example: “I noticed usually at night, but also some days, you turn the temperature down in what situations should I turn the temperature down? What other inputs (features) should I consider?”

Using the techniques herein, a smart voice device may also be able to learn things it otherwise may not be able to. For example, if the smart voice device is looking for common patterns in any of the aforementioned actions or purchases and the conviction drops below a certain threshold, it can ask the person if it should take on a particular action or additional autonomy without prompting, such as “It looks like you're normally changing the thermostat to colder on days when you have your exercise class, but not on days when it is cancelled; should I do this from now on and prepare the temperature to your liking?”

In some embodiments, processes 100 and/or 400 may determine (e.g., in response to a request) the surprisal and/or conviction of one or more data elements (e.g., of the smart voice system) or aspects (e.g., features of the data or parameters of the model) to potentially include in the smart voice system control computer-based reasoning model. The surprisal and/or conviction for the one or more smart voice system data elements or aspects may be determined and a determination may be made whether to include the one or more smart voice system data elements or aspects in the smart voice system control computer-based reasoning model based on the determined surprisal and/or conviction. While there are more sets of one or more smart voice system data elements or aspects to assess, the process 100 may return to determine whether more smart voice system data elements or aspects sets should be included. Once there are no more smart voice system data elements or aspects to consider, the process 100 can turn to controlling or causing 199 control of the smart voice system using the smart voice system control computer-based reasoning model.

In some embodiments, the techniques include performing the following until there are no more cases in a computer-based reasoning model with missing fields for which imputation is desired: determining 110 which cases have fields to impute (e.g., missing fields in the smart voice data elements) in the computer-based reasoning model and determining 120 conviction scores and/or imputation order information for the cases that have fields to impute. The techniques proceed by determining 130 for which cases to impute data and/or in what order to impute data based on the conviction scores and/or imputation order information. For each of the determined one or more cases with missing fields to impute data is imputed for the missing field, and the case is modified with the imputed data (e.g., adding the imputed metadata to the case file). Once there are no more cases or aspects for which to impute data, the process 100 can turn to controlling or causing 199 control of the smart voice analysis system using the smart voice-control computer-based reasoning model.

Controlling or causing 199 control of a smart voice system may be accomplished by process 400. For example, if the data elements are related to smart voice system actions, then the smart voice system control computer-based reasoning model trained on that data will control smart voice systems. Process 400 proceeds by receiving 410 a smart voice computer-based reasoning model. The process proceeds by receiving 420 a context. The smart voice computer-based reasoning model is then used to determine 430 an action to take. The action is then performed by the control system (e.g., caused by the smart voice computer-based reasoning system). If there are more 450 contexts to consider, then the system returns to receive 410 those contexts and otherwise ceases 460. In such embodiments, the smart voice computer-based reasoning model may be used to control a smart voice system. The chosen actions are then performed by a control system.

Control of Federated Devices

The processes 100 and/or 400 may also be applied in the context of federated devices in a system. For example, combining multiple inputs and forms of analysis, the techniques herein can recognize if there is something that should trigger action based on the state of the federated devices. For example, if the training data includes actions normally taken and/or statuses of federated devices, then an action to take could be an often-taken action in the certain (or related contexts). For example, in the context of a smart home with interconnected heating, cooling, appliances, lights, locks, etc., the training data could be what a particular user does at certain times of day and/or in particular sequences. For example, if, in a house, the lights in the kitchen are normally turned off after the stove has been off for over an hour and the dishwasher has been started, then when that context again occurs, but the kitchen light has not been turned off, the computer-based reasoning system may cause an action to be taken in the smart home federated systems, such as prompting (e.g., audio) whether the user of the system would like the kitchen lights to be turned off. As another example, training data may indicate that a user sets the house alarm and locks the door upon leaving the house (e.g., as detected via geofence). If the user leaves the geofenced location of the house and has not yet locked the door and/or set the alarm, the computer-based reasoning system may cause performance of an action such as inquiring whether it should lock the door and/or set an alarm. As yet another example, in the security context, the control may be for turning on/off cameras, or enact other security measures, such as sounding alarms, locking doors, or even releasing drones and the like. Training data may include previous logs and sensor data, door or window alarm data, time of day, security footage, etc. and when security measure were (or should have been) taken. For example, a context such as particular window alarm data for a particular basement window coupled with other data may be associated with an action of sounding an alarm, and when a context occurs related to that context, an alarm may be sounded.

In some embodiments, processes 100 and/or 400 may determine the surprisal and/or conviction of one or more data elements or aspects of the federated device control system for potential inclusion in the federated device control computer-based reasoning model. The surprisal and/or conviction for the one or more federated device control system data elements may be determined and a determination may be made whether to select or include the one or more federated device control system data elements in the federated device control computer-based reasoning model based on the determined surprisal and/or conviction. While there are more sets of one or more federated device control system data elements or aspects to assess, the process may return to determine whether more federated device control system data elements or aspect sets should be included. Once there are no more federated device control system data elements or aspects to consider, the process can turn to controlling or causing control of the federated device control system using the federated device control computer-based reasoning model.

In some embodiments, the techniques include performing the following until there are no more cases in a computer-based reasoning model with missing fields for which imputation is desired: determining 110 which cases have fields to impute (e.g., missing fields in the federated device data elements) in the computer-based reasoning model and determining 120 conviction scores and/or imputation order information for the cases that have fields to impute. The techniques proceed by determining 130 for which cases to impute data and/or in what order to impute data based on the conviction scores and/or imputation order information. For each of the determined one or more cases with missing fields to impute data is imputed for the missing field, and the case is modified with the imputed data (e.g., adding the imputed metadata to the case file). Once there are no more cases or aspects for which to impute data, the process 100 can turn to controlling or causing 199 control of the federated device analysis system using the federated device control computer-based reasoning model.

Controlling or causing 199 control of a federated device system may be accomplished by process 400. For example, if the data elements are related to federated device system actions, then the federated device control computer-based reasoning model trained on that data will control federated device control system. Process 400 proceeds by receiving 410 a federated device control computer-based reasoning model. The process proceeds by receiving 420 a context. The federated device control computer-based reasoning model is then used to determine 430 an action to take. The action is then performed by the control system (e.g., caused by the federated device control computer-based reasoning system). If there are more 450 contexts to consider, then the system returns to receive 410 those contexts and otherwise ceases 460. In such embodiments, the federated device control computer-based reasoning model may be used to control federated devices. The chosen actions are then performed by a control system.

Control and Automation of Experiments

The processes 100, 400 may also be used to control laboratory experiments. For example, many lab experiments today, especially in the biological and life sciences, but also in agriculture, pharmaceuticals, materials science and other fields, yield combinatorial increases, in terms of numbers, of possibilities and results. The fields of design of experiment, as well as many combinatorial search and exploration techniques are currently combined with statistical analysis. However, conviction-based techniques such as those herein can be used to guide a search for knowledge, especially if combined with utility or fitness functions. Automated lab experiments (including pharmaceuticals, biological and life sciences, material science, etc.) may have actuators and may put different chemicals, samples, or parts in different combinations and put them under different circumstances. Using conviction to guide the machines enables them to home in on learning how the system under study responds to different scenarios, and, for example, searching areas of greatest uncertainty (e.g., the areas with low conviction as discussed herein). Conceptually speaking, when the conviction or surprisal is combined with a fitness, utility, or value function, especially in a multiplicative fashion, then the combination is a powerful information theoretic approach to the classic exploration vs exploitation trade-offs that are made in search processes from artificial intelligence to science to engineering. Additionally, such a system can automate experiments where it can predict the most effective approach, homing in on the best possible, predictable outcomes for a specific knowledge base. Further, like in the other embodiments discussed herein, it could indicate (e.g., raise alarms) to human operators when the results are anomalous, or even tell which features being measured are most useful (so that they can be appropriately measured) or when measurements are not sufficient to characterize the outcomes. This is discussed extensively elsewhere herein. If the system has multiple kinds of sensors that have “costs” (e.g., monetary, time, computation, etc.) or cannot be all activated simultaneously, the feature entropies or convictions could be used to activate or deactivate the sensors to reduce costs or improve the distinguishability of the experimental results.

In the context of agriculture, growers may experiment with various treatments (plant species or varietals, crop types, seed planting densities, seed spacings, fertilizer types and densities, etc.) in order to improve yield and/or reduce cost. In comparing the effects of different practices (treatments), experimenters or growers need to know if the effects observed in the crop or in the field are simply a product of the natural variation that occurs in every ecological system, or whether those changes are truly a result of the new treatments. In order to ameliorate the confusion caused by overlapping crop, treatment, and field effects, different design types can be used (e.g., demonstration strip, replication control or measurement, randomized block, split plot, factorial design, etc.). Regardless, however, of the type of test design type used, determination of what treatment(s) to use is crucial to success. Using the techniques herein to guide treatment selection (and possible design type) enables experimenters and growers to home in on how the system under study responds to different treatments and treatment types, and, for example, searching areas of greatest uncertainty in the “treatment space” (e.g., what are the types of treatments about which little is known?). Conceptually, the combination of conviction or surprisal with a value, utility, or fitness function such as yield, cost, or a function of yield and cost, become a powerful information theoretic approach to the classic exploration vs exploitation trade-offs that are made in search processes from artificial intelligence to science to engineering. Growers can use this information to choose treatments balancing exploitation (e.g., doing things similar to what has produced high yields previously) and exploration (e.g., trying treatments unlike previous ones, with yet-unknown results). Additionally, the techniques can automate experiments on treatments (either in selection of treatments, designs, or robotic or automated planting using the techniques described herein) where it can predict the most effective approach, and automatically perform the planting or other distribution (e.g., of fertilizer, seed, etc.) required of to perform the treatment. Further, like in the other embodiments discussed herein, it could indicate (e.g., raise alarms) to human operators when the results are anomalous, or even tell which features being measured are most useful or when measurements are not useful to characterize the outcomes (e.g., and may possibly be discarded or no longer measured). If the system has types of sensors (e.g., soil moisture, nitrogen levels, sun exposure) that have “costs” (e.g., monetary, time, computation, etc.) or cannot be all collected or activated simultaneously, the feature entropies or convictions could be used to activate or deactivate the sensors to reduce costs while protecting the usefulness of the experimental results.

In some embodiments, processes 100, 400 may include determining (e.g., in response to a request) the surprisal and/or conviction of one or more data elements or aspects of the experiment control system. The surprisal and/or conviction for the one or more experiment control system data elements or aspects may be determined and a determination may be made whether to select or include the one or more experiment control system data elements or aspects in an experiment control computer-based reasoning model based on the determined surprisal and/or conviction. While there are more sets of one or more experiment control system data elements or aspects to assess, the process may return to determine whether more experiment control system data elements or aspects sets should be included. Once there are no more experiment control system data elements or aspects to consider, the process can cause 199 control of the experiment control system using the experiment control computer-based reasoning model.

In some embodiments, the techniques include performing the following until there are no more cases in a computer-based reasoning model with missing fields for which imputation is desired: determining 110 which cases have fields to impute (e.g., missing fields in the experiment control data elements) in the computer-based reasoning model and determining 120 conviction scores and/or imputation order information for the cases that have fields to impute. The techniques proceed by determining 130 for which cases to impute data and/or in what order to impute data based on the conviction scores and/or imputation order information. For each of the determined one or more cases with missing fields to impute data is imputed for the missing field, and the case is modified with the imputed data (e.g., adding the imputed metadata to the case file). Once there are no more cases or aspects for which to impute data, the process 100 can turn to controlling or causing 199 control of the experiment control system using the experiment control computer-based reasoning model.

Controlling or causing 199 control of an experiment control system may be accomplished by process 400. For example, if the data elements are related to experiment control system actions, then the experiment control computer-based reasoning model trained on that data will control experiment control system. Process 400 proceeds by receiving 410 an experiment control computer-based reasoning model. The process proceeds by receiving 420 a context. The experiment control computer-based reasoning model is then used to determine 430 an action to take. The action is then performed by the control system (e.g., caused by the experiment control computer-based reasoning system). If there are more 450 contexts to consider, then the system returns to receive 410 those contexts and otherwise ceases 460. In such embodiments, the experiment control computer-based reasoning model may be used to control experiment. The chosen actions are then performed by a control system.

Control of Energy Transfer Systems

The processes 100 and/or 400 may also be applied in the context of control systems for energy transfer. For example, a building may have numerous energy sources, including solar, wind, grid-based electrical, batteries, on-site generation (e.g., by diesel or gas), etc. and may have many operations it can perform, including manufacturing, computation, temperature control, etc. The techniques herein may be used to control when certain types of energy are used and when certain energy consuming processes are engaged. For example, on sunny days, roof-mounted solar cells may provide enough low-cost power that grid-based electrical power is discontinued during a particular time period while costly manufacturing processes are engaged. On windy, rainy days, the overhead of running solar panels may overshadow the energy provided, but power purchased from a wind-generation farm may be cheap, and only essential energy consuming manufacturing processes and maintenance processes are performed.

In some embodiments, processes 100 and/or 400 may determine (e.g., in response to a request) the surprisal and/or conviction of one or more data elements or aspects of the energy transfer system. The surprisal and/or conviction for the one or more energy transfer system data elements or aspects may be determined and a determination may be made whether to select or include the one or more energy transfer system data elements or aspects in energy control computer-based reasoning model based on the determined surprisal and/or conviction. While there are more sets of one or more energy transfer system data elements or aspects to assess, the process may return to determine whether more energy transfer system data elements or aspects should be included. Once there are no more energy transfer system data elements or aspects to consider, the process can turn to controlling or causing control of the energy transfer system using the energy control computer-based reasoning model.

In some embodiments, the techniques include performing the following until there are no more cases in a computer-based reasoning model with missing fields for which imputation is desired: determining 110 which cases have fields to impute (e.g., missing fields in the energy control data elements) in the computer-based reasoning model and determining 120 conviction scores and/or imputation order information for the cases that have fields to impute. The techniques proceed by determining 130 for which cases to impute data and/or in what order to impute data based on the conviction scores and/or imputation order information. For each of the determined one or more cases with missing fields to impute data is imputed for the missing field, and the case is modified with the imputed data (e.g., adding the imputed metadata to the case file). Once there are no more cases or aspects for which to impute data, the process 100 can turn to controlling or causing 199 control of the energy transfer system using the energy transfer control computer-based reasoning model.

Controlling or causing 199 control of an energy transfer system may be accomplished by process 400. For example, if the data elements are related to energy transfer system actions, then the energy control computer-based reasoning model trained on that data will control energy transfer system. Process 400 proceeds by receiving 410 an energy control computer-based reasoning model. The process proceeds by receiving 420 a context. The energy control computer-based reasoning model is then used to determine 430 an action to take. The action is then performed by the control system (e.g., caused by the energy control computer-based reasoning system). If there are more 450 contexts to consider, then the system returns to receive 410 those contexts and otherwise ceases 460. In such embodiments, the energy control computer-based reasoning model may be used to control energy. The chosen actions are then performed by a control system.

Health Care Decision Making, Prediction, and Fraud Protection

The processes 100 and/or 400 may also be used for health care decision making, prediction (such as outcome prediction), and/or fraud detection. For example, some health insurers require pre-approval, pre-certification, pre-authorization, and/or reimbursement for certain types of healthcare procedures, such as healthcare services, administration of drugs, surgery, hospital visits, etc. When analyzing pre-approvals, a health care professional must contact the insurer to obtain their approval prior to administering care, or else the health insurance company may not cover the procedure. Not all services require pre-approval, but many may, and which require it can differ among insurers. Health insurance companies may make determinations including, but not necessarily limited to, whether a procedure is medically necessary, whether it is duplicative, whether it follows currently-accepted medical practice, whether there are anomalies in the care or its procedures, whether there are anomalies or errors with the health care provider or professional, etc.

In some embodiments, a health insurance company may have many “features” of data on which health care pre-approval or reimbursement decisions are determined by human operators. These features may include diagnosis information, type of health insurance, requesting health care professional and facility, frequency and/or last claim of the particular type, etc. The data on previous decisions can be used to train the computer-based reasoning system. The techniques herein may be used to guide the health care decision making process. For example, when the computer-based reasoning model determines, with high conviction or confidence, that a procedure should be pre-approved or reimbursed, it may pre-approve or reimburse the procedure without further review. In some embodiments, when the computer-based reasoning model has low conviction re whether or not to pre-approve a particular procedure, it may flag it for human review (including, e.g., sending it back to the submitting organization for further information). In some embodiments, some or all of the rejections of procedure pre-approval or reimbursement may be flagged for human review.

Further, in some embodiments, the techniques herein can be used to flag trends, anomalies, and/or errors. For example, as explained in detail elsewhere herein, the techniques can be used to determine, for example, when there are anomalies for a request for pre-approval, diagnoses, reimbursement requests, etc. with respect to the computer-based reasoning model trained on prior data. When the anomaly is detected, (e.g., outliers, such as a procedure or prescription has been requested outside the normal range of occurrences per time period, for an individual that is outside the normal range of patients, etc.; and/or what may be referred to as “inliers”—or “contextual outliers,” such as too frequently (or rarely) occurring diagnoses, procedures, prescriptions, etc.), the pre-approval, diagnosis, reimbursement request, etc. can be flagged for further review. In some cases, these anomalies could be errors (e.g., and the health professional or facility may be contacted to rectify the error), acceptable anomalies (e.g., patients that need care outside of the normal bounds), or unacceptable anomalies. Additionally, in some embodiments, the techniques herein can be used to determine and flag trends (e.g., for an individual patient, set of patients, health department or facility, region, etc.). The techniques herein may be useful not only because they can automate and/or flag pre-approval decision, reimbursement requests, diagnosis, etc., but also because the trained computer-based reasoning model may contain information (e.g., prior decision) from multiple (e.g., 10 s, 100 s, 1000 s, or more) prior decision makers. Consideration of this large amount of information may be untenable for other approaches, such as human review.

The techniques herein may also be used to predict adverse outcomes in numerous health care contexts. The computer-based reasoning model may be trained with data from previous adverse events, and perhaps from patients that did not have adverse events. The trained computer-based reasoning system can then be used to predict when a current or prospective patient or treatment is likely to cause an adverse event. For example, if a patient arrives at a hospital, the patient's information and condition may be assessed by the computer-based reasoning model using the techniques herein in order to predict whether an adverse event is probable (and the conviction of that determination). As a more specific example, if a septuagenarian with a history of low blood pressure is admitted for monitoring a heart murmur, the techniques herein may flag that patient for further review. In some embodiments, the determination of a potential adverse outcome may be an indication of one or more possible adverse events, such as a complication, having an additional injury, sepsis, increased morbidity, and/or getting additionally sick, etc. Returning to the example of the septuagenarian with a history of low blood pressure, the techniques herein may indicate that, based on previous data, the possibility of a fall in the hospital is unduly high (possibly with high conviction). Such information can allow the hospital to try to ameliorate the situation and attempt to prevent the adverse event before it happens.

In some embodiments, the techniques herein include assisting in diagnosis and/or diagnosing patients based on previous diagnosis data and current patient data. For example, a computer-based reasoning model may be trained with previous patient data and related diagnoses using the techniques herein. The diagnosis computer-based reasoning model may then be used in order to suggest one or more possible diagnoses for the current patient. As a more specific example, a septuagenarian may present with specific attributes, medical history, family history, etc. This information may be used as the input context to the diagnosis computer-based reasoning system, and the diagnosis computer-based reasoning system may determine one or more possible diagnoses for the septuagenarian. In some embodiments, those possible diagnoses may then be assessed by medical professionals. The techniques herein may be used to diagnose any condition, including, but not limited to breast cancer, lung cancer, colon cancer, prostate cancer, bone metastases, coronary artery disease, congenital heart defect, brain pathologies, Alzheimer's disease, and/or diabetic retinopathy.

In some embodiments, the techniques herein may be used to generate synthetic data that mimics, but does not include previous patient data. This synthetic data generation is available for any of the uses of the techniques described herein (manufacturing, image labelling, self-driving vehicles, etc.), and can be particularly important in circumstances where using user data (such as patient health data) in a model may be contrary to policy or regulation. As discussed elsewhere herein, the synthetic data can be generated to directly mimic the characteristics of the patient population, or more surprising data can be generated (e.g., higher surprisal) in order to generate more data in the edge cases, all without a necessity of including actual patient data.

In some embodiments, processes 100, 400 may include determining (e.g., in response to a request) the surprisal and/or conviction of one or more data elements or aspects of the health care system. The surprisal or conviction for the one or more health care system data elements or aspects may be determined and a determination may be made whether to select or include the one or more health care system data elements or aspects in a health care system computer-based reasoning model based on the determined surprisal and/or conviction. While there are more sets of one or more health care system data elements or aspects to assess, the process may return to determine whether more health care system data elements or aspects should be included. Once there are no more health care system data elements or aspects to consider included in the model, the process can turn to controlling the health care computer-based reasoning system using the health care system computer-based reasoning model.

In some embodiments, the techniques include performing the following until there are no more cases in a computer-based reasoning model with missing fields for which imputation is desired: determining 110 which cases have fields to impute (e.g., missing fields in the health care system data elements) in the computer-based reasoning model and determining 120 conviction scores and/or imputation order information for the cases that have fields to impute. The techniques proceed by determining 130 for which cases to impute data and/or in what order to impute data based on the conviction scores and/or imputation order information. For each of the determined one or more cases with missing fields to impute data is imputed for the missing field, and the case is modified with the imputed data (e.g., adding the imputed data to the case file). Once there are no more cases or aspects for which to impute data, the process 100 can turn to controlling or causing 199 control of the health care system using the health care system computer-based reasoning model.

In some embodiments, processes 100 and/or 400 may determine (e.g., in response to a request) the search result (e.g., k nearest neighbors, most probable cases in gaussian process regression, etc.) in the computer-based reasoning model for use in the health care system computer-based reasoning model. Based on those search results, the process can cause 199 control of a health care computer-based reasoning system using process 400. For example, if the data elements are related to health care system actions, then the health care system computer-based reasoning model trained on that data will control the health care system. Process 400 proceeds by receiving 410 a health care system computer-based reasoning model. The process proceeds by receiving 420 a context. The health care system computer-based reasoning model is then used to determine 430 an action to take. The action is then performed by the control system (e.g., caused by the health care system computer-based reasoning system). If there are more 450 contexts to consider, then the system returns to receive 410 those contexts and otherwise ceases 460. In such embodiments, the health care system computer-based reasoning model may be used to assess health care decisions, predict outcomes, etc. In some embodiments, the chosen action(s) are then performed by a control system.

Financial Decision Making, Prediction, and Fraud Protection

The processes 100 and/or 400 may also be used for financial decision making, prediction (such as outcome or performance prediction), and/or fraud detection. For example, some financial systems require approval, certification, authorization, and/or reimbursement for certain types of financial transactions, such as loans, lines of credit, credit or charge approvals, etc. When analyzing approvals, a financial professional may determine, as one example, whether to approve prior to loaning money. Not all services or transactions require approval, but many may, and which require it can differ among financial system or institutions. Financial transaction companies may make determinations including, but not necessarily limited to, whether a loan appears to be viable, whether a charge is duplicative, whether a loan, charge, etc. follows currently-accepted practice, whether there are anomalies associated with the loan or charge, whether there are anomalies or errors with the any party to the loan, etc.

In some embodiments, a financial transaction company may have many “features” of data on which financial system decisions are determined by human operators. These features may include credit score, type of financial transaction (loan, credit card transaction, etc.), requesting financial system professional and/or facility (e.g., what bank, merchant, or other requestor), frequency and/or last financial transaction of the particular type, etc. The data on previous decisions can be used to train the computer-based reasoning system. The techniques herein may be used to guide the financial system decision making process. For example, when the computer-based reasoning model determines, with high conviction or confidence, that a financial transaction should be approved (e.g. with high conviction), it may the approve the transaction without further review (e.g., by a human operator). In some embodiments, when the computer-based reasoning model has low conviction re whether or not to approve a particular transaction, it may flag it for human review (including, e.g., sending it back to the submitting organization for further information or analysis). In some embodiments, some or all of the rejections of approvals may be flagged for human review.

Further, in some embodiments, the techniques herein can be used to flag trends, anomalies, and/or errors. For example, as explained in detail elsewhere herein, the techniques can be used to determine, for example, when there are anomalies for a request for approval, etc. with respect to the computer-based reasoning model trained on prior data. When the anomaly is detected, (e.g., outliers, such as a transaction has been requested outside the normal range of occurrences per time period, for an individual that is outside the normal range of transactions or approvals, etc.; and/or what may be referred to as “inliers”- or “contextual outliers,” such as too frequently (or rarely) occurring types of transactions or approvals, unusual densities or changes to densities of the data, etc.), the approval may be flagged for further review. In some cases, these anomalies could be errors (e.g., and the financial professional or facility may be contacted to rectify the error), acceptable anomalies (e.g., transactions or approvals are legitimate, even if outside of the normal bounds), or unacceptable anomalies. Additionally, in some embodiments, the techniques herein can be used to determine and flag trends (e.g., for an individual customer or financial professional, set of individuals, financial department or facility, systems, etc.). The techniques herein may be useful not only because they can automate and/or flag approval decisions, transactions, etc., but also because the trained computer-based reasoning model may contain information (e.g., prior decision) from multiple (e.g., 10 s, 100 s, 1000 s, or more) prior decision makers. Consideration of this large amount of information may be untenable for other approaches, such as human review.

In some embodiments, the techniques herein may be used to generate synthetic data that mimics, but does not include previous financial data. This synthetic data generation is available for any of the uses of the techniques described herein (manufacturing, image labelling, self-driving vehicles, etc.), and can be particularly important in circumstances where using user data (such as financial data) in a model may be contrary to contract, policy, or regulation. As discussed elsewhere herein, the synthetic data can be generated to directly mimic the characteristics of the financial transactions and/or users, or more surprising data can be generated (e.g., higher surprisal) in order to generate more data in the edge cases, all without including actual financial data.

In some embodiments, processes 100 and/or 400 may include determining (e.g., in response to a request) the surprisal and/or conviction of one or more data elements or aspects of the financial system. The surprisal and/or conviction for the one or more financial system data elements or aspects may be determined and a determination may be made whether to select or include the one or more financial system data elements or aspects in a financial system computer-based reasoning model based on the determined surprisal and/or conviction. While there are more sets of one or more financial system data elements or aspects to assess, the process may return to determine whether more financial system data elements or aspects should be included. Once there are no more financial system data elements or aspects to consider included in the model, the process can turn to controlling the financial system computer-based reasoning system using the financial system computer-based reasoning model.

In some embodiments, the techniques include performing the following until there are no more cases in a computer-based reasoning model with missing fields for which imputation is desired: determining 110 which cases have fields to impute (e.g., missing fields in the financial system data elements) in the computer-based reasoning model and determining 120 conviction scores and/or imputation order information for the cases that have fields to impute. The techniques proceed by determining 130 for which cases to impute data and/or in what order to impute data based on the conviction scores and/or imputation order information. For each of the determined one or more cases with missing fields to impute data is imputed for the missing field, and the case is modified with the imputed data (e.g., adding the imputed data to the case file). Once there are no more cases or aspects for which to impute data, the process 100 can turn to controlling or causing 199 control of the financial system using the financial system computer-based reasoning model.

In some embodiments, processes 100 and/or 400 may determine (e.g., in response to a request) the search result (e.g., k nearest neighbors, most probable cases in gaussian process regression, etc.) in the computer-based reasoning model for use in the financial system computer-based reasoning model. Based on those search results, the process can cause control of a financial system computer-based reasoning system using process 400. For example, if the data elements are related to financial system actions, then the financial system computer-based reasoning model trained on that data will control the financial system. Process 400 proceeds by receiving 410 a financial system computer-based reasoning model. The process proceeds by receiving 420 a context. The financial system computer-based reasoning model is then used to determine 430 an action to take. The action is then performed by the control system (e.g., caused by the financial system computer-based reasoning system). If there are more 450 contexts to consider, then the system returns to receive 410 those contexts and otherwise ceases 460. In such embodiments, the financial system computer-based reasoning model may be used to assess financial system decisions, predict outcomes, etc. In some embodiments, the chosen action(s) are then performed by a control system.

Real Estate Future Value and Valuation Prediction

The techniques herein may also be used for real estate value estimation. For example, the past values and revenue from real estate ventures may be used as training data. This data may include, in addition to value (e.g., sale or resale value), compound annual growth rate (“CAGR”), zoning, property type (e.g., multifamily, Office, Retail, Industrial), adjacent business and types, asking rent (e.g., rent per square foot (“sqft”) for each of Office, Retail, Industrial, etc. and/or per unit (for multifamily buildings), further, this may be based on all properties within the selected property type in a particular geography, for example), capitalization rate (or “cap rate” based on all properties within selected property type in a geography), demand (which may be quantified as occupied stock), market capitalization (e.g., an average modeled price per sqft multiplied by inventory sqft of the given property type and/or in a given geography), net absorption (net change in demand for a rolling 12 month period), net completions (e.g., net change in inventory sqft (Office, Retail, Industrial) or units (Multifamily) for a period of time, such as analyzed data element(s) rolling 12 month period), occupancy (e.g., Occupied sqft/total inventory sqft, 100%-vacancy %, etc.), stock (e.g., inventory square footage (Office, Retail, Industrial) or units (Multifamily), revenue (e.g., revenue generated by renting out or otherwise using a piece of real estate), savings (e.g., tax savings, depreciation), costs (e.g., taxes, insurance, upkeep, payments to property managers, costs for findings tenants, property managers, etc.), geography and geographic location (e.g., views of water, distance to shopping, walking score, proximity to public transportation, distance to highways, proximity to job centers, proximity to local universities, etc.), building characteristics (e.g., date built, date renovated, etc.), property characteristics (e.g., address, city, state, zip, property type, unit type(s), number of units, numbers of bedrooms and bathrooms, square footage(s), lot size(s), assessed value(s), lot value(s), improvements value(s), etc. —possibly including current and past values), real estate markets characteristics (e.g., local year-over-year growth, historical year-over-year growth), broader economic information (e.g., gross domestic product growth, consumer sentiment, economic forecast data), local economic information (e.g., local economic growth, average local salaries and growth, etc.), local demographics (e.g., numbers of families, couples, single people, number of working-age people, numbers or percentage of people with at different education, salary, or savings levels, etc.). The techniques herein may be used to train a real estate computer-based reasoning model based on previous properties. Once the real estate computer-based reasoning system has been trained, then input properties may be analyzed using the real estate reasoning system. Using the techniques herein, the surprisal and/or conviction of the training data can be used to build an real estate computer-based reasoning system that balances the size of the computer-based reasoning model with the information that each additional property record (or set of records) provides to the model.

The techniques herein may be used to predict performance of real estate in the future. For example, based on the variables associated discussed here, that are related, e.g., with various geographies, property types, and markets, the techniques herein may be used to find property types and geographies with the highest expected value or return (e.g., as CAGR). As a more specific example, a model of historical CAGR with asking rent, capitalization rate, demand, net absorption, net completions, occupancy, stock, etc. can be trained. That model may be used, along with more current data, to predict the CAGR of various property types and/or geographies over the coming X years (e.g., 2, 3, 5, or 10 years). Such information may be useful for predicting future value for properties and/or automated decision making.

As another example, using the techniques herein, a batch of available properties may be given as input to the real estate computer-based reasoning systems, and the real estate computer-based reasoning system may be used to determine what properties are likely to be good investments. In some embodiments, the predictions of the computer-based reasoning system may be used to purchase properties. Further, as discussed extensively herein, explanations may be provided for the decisions. Those explanation may be used by a controllable system to make investment decisions and/or by a human operator to review the investment predictions.

In some embodiments, processes 100 and/or 400 may include determining the surprisal and/or conviction of each input real estate data case (or multiple real estate data cases) with respect to the associated labels or of the aspects of the computer-based reasoning model. The surprisal and/or conviction for the one or more real estate data cases may be determined and a determination may be made whether to select or include the one or more real estate data cases in the real estate computer-based reasoning model based on the determined surprisal and/or conviction. While there are more sets of one or more real estate data cases to assess, the process may return to determine whether more real estate data case sets should be included or whether aspects should be included and/or changed in the model. Once there are no more training cases to consider, the process can turn to controlling predicting real estate investments information for possible use in purchasing real estate using the real estate computer-based reasoning model.

In some embodiments, the techniques include performing the following until there are no more cases in a computer-based reasoning model with missing fields for which imputation is desired: determining 110 which cases have fields to impute (e.g., missing fields in the real estate system data elements) in the computer-based reasoning model and determining 120 conviction scores and/or imputation order information for the cases that have fields to impute. The techniques proceed by determining 130 for which cases to impute data and/or in what order to impute data based on the conviction scores and/or imputation order information. For each of the determined one or more cases with missing fields to impute data is imputed for the missing field, and the case is modified with the imputed data (e.g., adding the imputed data to the case file). Once there are no more cases or aspects for which to impute data, the process 100 can turn to controlling or causing 199 control of the real estate system using the real estate system computer-based reasoning model.

In some embodiments, processes 100 and/or 400 may determine (e.g., in response to a request) the search result (e.g., k nearest neighbors, most probable cases in gaussian process regression, etc.) in the computer-based reasoning model for use in the real estate computer-based reasoning model. Based on those search results, the process can cause 199 control of a real estate system, using, for example, process 400. For example, the training data elements are related to real estate, and the real estate computer-based reasoning model trained on that data will determined investment value(s) for real estate data cases (properties) under consideration. These investment values may be any appropriate value, such as CAGR, monthly income, resale value, income or resale value based on refurbishment or new development, net present value of one or more of the preceding, etc. In some embodiments, process 400 begins by receiving 410 a real estate computer-based reasoning model. The process proceeds by receiving 420 properties under consideration for labeling and/or predicting value(s) for the investment opportunity. The real estate computer-based reasoning model is then used to determine 430 values for the real estate under consideration. The prediction(s) for the real estate is (are) then made 440. If there are more 450 properties to consider, then the system returns to receive 410 data on those properties and otherwise ceases 460. In some embodiments, the real estate computer-based reasoning model may be used to determine which training properties are “closest” (or most similar) to the incoming property or property types and/or geographies predicted as high value. The investment value(s) for the properties under consideration may then be determined based on the “closest” properties or property types and/or geographies.

Cybersecurity

The processes 100 and/or 400 may also be used for cybersecurity analysis. For example, a cybersecurity company or other organization may want to perform threat (or anomalous behavior) analysis, and in particular may want explanation data associated with the threat or anomalous behavior analysis (e.g., why was a particular event, user, etc. identified as a threat or not a threat?). The computer-based reasoning model may be trained using known threats/anomalous behavior and features associated with those threats or anomalous behavior. Data that represents neither a threat nor anomalous behavior (e.g., non-malicious access attempts, non-malicious emails, etc.) may also be used to train the computer-based reasoning model. In some embodiments, when a new entity, user, packet, payload, routing attempt, access attempt, log file, etc. is ready for assessment, the features associated with that new entity, user, packet, payload, routing attempt, access attempt, log file, etc. may be used as input in the trained cybersecurity computer-based reasoning system. The cybersecurity computer-based reasoning system may then determine the probability or likelihood that the entity, user, packet, payload, routing attempt, access attempt, pattern in the log file, etc. is or represents a threat or anomalous behavior. Further, explanation data, such as a conviction measures, training data used to make a decision etc., can be used to mitigate the threat or anomalous behavior and/or be provided to a human operator in order to further assess the potential threat or anomalous behavior.

Any type of cybersecurity threat or anomalous behavior can be analyzed and detected, such as denial of service (DOS), distributed DOS (DDoS), brute-force attacks (e.g., password breach attempts), compromised credentials, malware, insider threats, advanced persistent threats, phishing, spear phishing, etc. and/or anomalous traffic volume, bandwidth use, protocol use, behavior of individuals and/or accounts, log file pattern, access or routing attempt, etc. In some embodiments the cybersecurity threat is mitigated (e.g., access is suspended, etc.) while the threat is escalated to a human operator. As a more specific example, if an email is received by the email server, the email may be provided as input to the trained cybersecurity computer-based reasoning model. The cybersecurity computer-based reasoning model may indicate that the email is a potential threat (e.g., detecting and then indicating that email includes a link to a universal resource locator that is different from the universal resource location displayed in the text of the email). In some embodiments, this email may be automatically deleted, may be quarantined, and/or flagged for review.

In some embodiments, processes 100 and/or 400 may include determining (e.g., in response to a request) the surprisal and/or conviction of one or more data elements or aspects of the cybersecurity system. The surprisal or conviction for the one or more cybersecurity system data elements or aspects may be determined and a determination may be made whether to select or include the one or more cybersecurity system data elements or aspects in a cybersecurity system computer-based reasoning model based on the determined surprisal and/or conviction. While there are more sets of one or more cybersecurity system data elements or aspects to assess, the process may return to determine whether more cybersecurity system data elements or aspects should be included. Once there are no more cybersecurity system data elements or aspects to consider, the process can turn to controlling the cybersecurity computer-based reasoning system using the cybersecurity system computer-based reasoning model.

In some embodiments, the techniques include performing the following until there are no more cases in a computer-based reasoning model with missing fields for which imputation is desired: determining 110 which cases have fields to impute (e.g., missing fields in the cybersecurity system data elements) in the computer-based reasoning model and determining 120 conviction scores and/or imputation order information for the cases that have fields to impute. The techniques proceed by determining 130 for which cases to impute data and/or in what order to impute data based on the conviction scores and/or imputation order information. For each of the determined one or more cases with missing fields to impute data is imputed for the missing field, and the case is modified with the imputed data (e.g., adding the imputed data to the case file). Once there are no more cases or aspects for which to impute data, the process 100 can turn to controlling or causing 199 control of the cybersecurity system using the cybersecurity system computer-based reasoning model.

In some embodiments, processes 100 and/or 400 may determine (e.g., in response to a request) the search result (e.g., k nearest neighbors, most probable cases in gaussian process regression, etc.) in the computer-based reasoning model for use in the cybersecurity system computer-based reasoning model. Based on those search results, the process can cause 199 control of a cybersecurity computer-based reasoning system using process 400. For example, if the data elements are related to cybersecurity system actions, then the cybersecurity system computer-based reasoning model trained on that data will control the cybersecurity system (e.g., quarantine, delete, or flag for review, entities, data, network traffic, etc.). Process 400 proceeds by receiving 410 a cybersecurity system computer-based reasoning model. The process proceeds by receiving 420 a context. The cybersecurity system computer-based reasoning model is then used to determine 430 an action to take. The action is then performed by the control system (e.g., caused by the cybersecurity system computer-based reasoning system). If there are more 450 contexts to consider, then the system returns to receive 410 those contexts and otherwise ceases 460. In such embodiments, the cybersecurity system computer-based reasoning model may be used to assess cybersecurity threats, etc. In some embodiments, the chosen action(s) are then performed by a control system.

Example Control Hierarchies

In some embodiments, the technique herein may use a control hierarchy to control systems and/or cause actions to be taken (e.g., as part of controlling or causing 199 control of in FIG. 1). There are numerous example control hierarchies and many types of systems to control, and hierarchy for vehicle control is presented below. In some embodiments, only a portion of this control hierarchy is used. It is also possible to add levels to (or remove levels from) the control hierarchy.

An example control hierarchy for controlling a vehicle could be:

-   -   Primitive Layer—Active vehicle abilities (accelerate,         decelerate), lateral, elevation, and orientation movements to         control basic vehicle navigation     -   Behavior Layer—Programmed vehicle behaviors which prioritize         received actions and directives and prioritize the behaviors in         the action.     -   Unit Layer—Receives orders from command layer, issues         moves/directives to the behavior layer.     -   Command Layers (hierarchical) —Receives orders and gives orders         to elements under its command, which may be another command         layer or unit layer.         Example Data Cases, Data Elements, Contexts, and Operational         Situations

In some embodiments, the cases, data cases, or data elements may include context data and action data in context-action pairs. Further, cases may relate to control of a vehicle, control of a smart voice control, health system, real estate system, image labelling systems, or any of the other examples herein. Consider an example of data collected while a driver, Alicia, drives around a city. The collected data could be context and action data where the actions taken can include high-level actions (e.g., drive to next intersection, exit the highway, take surface roads, etc.), mid-level actions (e.g., turn left, turn right, change lanes) and/or low-level actions (e.g., accelerate, decelerate, etc.). The contexts can include any information related to the vehicle (e.g. time until impact with closest object(s), speed, course heading, breaking distances, vehicle weight, etc.), the driver (pupillary dilation, heart rate, attentiveness, hand position, foot position, etc.), the environment (speed limit and other local rules of the road, weather, visibility, road surface information, both transient such as moisture level as well as more permanent, such as pavement levelness, existence of potholes, etc.), traffic (congestion, time to a waypoint, time to destination, availability of alternate routes, etc.), and the like. These input data (e.g., context-action pairs for training a context-based reasoning system or input training contexts with outcome actions for training a machine learning system) can be saved and later used to help control a compatible vehicle in a compatible operational situation. The operational situation of the vehicle may include any relevant data related to the operation of the vehicle. In some embodiments, the operational situation may relate to operation of vehicles by particular individuals, in particular geographies, at particular times, and in particular conditions. For example, the operational situation may refer to a particular driver (e.g., Alicia or Carole). Alicia may be considered a cautious car driver, and Carole a faster driver. As noted above, and in particular, when approaching a stop sign, Carole may coast in and then brake at the last moment, while Alicia may slow down earlier and roll in. As another example of an operational situation, Bob may be considered the “best pilot” for a fleet of helicopters, and therefore his context and actions may be used for controlling self-flying helicopters.

In some embodiments, the operational situation may relate to the environment in which the system is operating. In the vehicle context, the locale may be a geographic area of any size or type, and may be determined by systems that utilize machine learning. For example, an operational situation may be “highway driving” while another is “side street driving”. An operational situation may be related to an area, neighborhood, city, region, state, country, etc. For example, one operational situation may relate to driving in Raleigh, N.C. and another may be driving in Pittsburgh, Pa. An operational situation may relate to safe or legal driving speeds. For example, one operational situation may be related to roads with forty-five miles per hour speed limits, and another may relate to turns with a recommended speed of 20 miles per hour. The operational situation may also include aspects of the environment such as road congestion, weather or road conditions, time of day, etc. The operational situation may also include passenger information, such as whether to hurry (e.g., drive faster), whether to drive smoothly, technique for approaching stop signs, red lights, other objects, what relative velocity to take turns, etc. The operational situation may also include cargo information, such as weight, hazardousness, value, fragility of the cargo, temperature sensitivity, handling instructions, etc.

In some embodiments, the context and action may include system maintenance information. In the vehicle context, the context may include information for timing and/or wear-related information for individual or sets of components. For example, the context may include information on the timing and distance since the last change of each fluid, each belt, each tire (and possibly when each was rotated), the electrical system, interior and exterior materials (such as exterior paint, interior cushions, passenger entertainment systems, etc.), communication systems, sensors (such as speed sensors, tire pressure monitors, fuel gauges, compasses, global positioning systems (GPS), RADARs, LiDARs, cameras, barometers, thermal sensors, accelerometers, strain gauges, noise/sound measurement systems, etc.), the engine(s), structural components of the vehicle (wings, blades, struts, shocks, frame, hull, etc.), and the like. The action taken may include inspection, preventative maintenance, and/or a failure of any of these components. As discussed elsewhere herein, having context and actions related to maintenance may allow the techniques to predict when issues will occur with future vehicles and/or suggest maintenance. For example, the context of an automobile may include the distance traveled since the timing belt was last replaced. The action associated with the context may include inspection, preventative replacement, and/or failure of the timing belt. Further, as described elsewhere herein, the contexts and actions may be collected for multiple operators and/or vehicles. As such, the timing of inspection, preventative maintenance and/or failure for multiple automobiles may be determined and later used for predictions and messaging.

Causing performance of an identified action can include causing a control system to control the target system based on the identified action. In the self-controlled vehicle context, this may include sending a signal to a real car, to a simulator of a car, to a system or device in communication with either, etc. Further, the action to be caused can be simulated/predicted without showing graphics, etc. For example, the techniques might cause performance of actions in the manner that includes, determining what action would be take, and determining whether that result would be anomalous, and performing the techniques herein based on the determination that such state would be anomalous based on that determination, all without actually generating the graphics and other characteristics needed for displaying the results needed in a graphical simulator (e.g., a graphical simulator might be similar to a computer game).

Example of Certainty and Conviction

In some embodiments, certainty score is a broad term encompassing it plain and ordinary meaning, including the certainty (e.g., as a certainty function) that a particular set of data fits a model, the confidence that a particular set of data conforms to the model, or the importance of a feature or case with regard to the model. Determining a certainty score for a particular case can be accomplished by removing the particular case from the case-based or computer-based reasoning model and determining the conviction score of the particular case based on an entropy measure associated with adding that particular case back into the model. Any appropriate entropy measure, variance, confidence, and/or related method can be used for making this determination, such as the ones described herein. In some embodiments, certainty or conviction is determined by the expected information gain of adding the case to the model divided by the actual information gain of adding the case. For example, in some embodiments, certainty or conviction may be determined based on Shannon Entropy, Renyi entropy, Hartley entropy, min entropy, Collision entropy, Renyi divergence, diversity index, Simpson index, Gini coefficient, Kullback-Leibler divergence, Fisher information, Jensen-Shannon divergence, Symmetrised divergence. In some embodiments, certainty scores are conviction scores and are determined by calculating the entropy, comparing the ratio of entropies, and/or the like.

In some embodiments, the conviction of a case may be computed based on looking only at the K nearest neighbors when adding the feature back into the model. The K nearest neighbors can be determined using any appropriate distance measure, including use of Euclidean distance, 1—Kronecker delta, Minkowski distance, Damerau-Levenshtein distance, and/or any other distance measure, metric, pseudometric, premetric, index, or the like. In some embodiments, influence functions are used to determine the importance of a feature or case.

In some embodiments, determining certainty or conviction scores can include determining the conviction of each feature of multiple features of the cases in the computer-based reasoning model. In this context the word “feature” is being used to describe a data field as across all or some of the cases in the computer-based reasoning model. The word “field,” in this context, is being used to describe the value of an individual case for a particular feature. For example, a feature for a theoretical computer-based reasoning model for self-driving cars may be “speed”. The field value for a particular case for the feature of speed may be the actual speed, such as thirty-five miles per hour.

Returning to determining certainty or conviction scores, in some embodiments, determining the conviction of a feature may be accomplished by removing the feature from the computer-based reasoning model and determining a conviction score of the feature based on an entropy measure associated with adding the feature back into the computer-based reasoning model. For example, returning to the example above, removing a speed feature from a self-driving car computer-based reasoning model could include removing all of the speed values (e.g., fields) from cases from the computer-based reasoning model and determining the conviction of adding speed back into the computer-based reasoning model. The entropy measure used to determine the conviction score for the feature can be any appropriate entropy measure, such as those discussed herein. In some embodiments, the conviction of a feature may also be computed based on looking only at the K nearest neighbors when adding the feature back into the model. In some embodiments, the feature is not actually removed, but only temporarily excluded.

Hardware Overview

According to some embodiments, the techniques described herein are implemented by one or more special-purpose computing devices. The special-purpose computing devices may be hard-wired to perform the techniques, or may include digital electronic devices such as one or more application-specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs) that are persistently programmed to perform the techniques, or may include one or more general purpose hardware processors programmed to perform the techniques pursuant to program instructions in firmware, memory, other storage, or a combination. Such special-purpose computing devices may also combine custom hard-wired logic, ASICs, or FPGAs with custom programming to accomplish the techniques. The special-purpose computing devices may be desktop computer systems, portable computer systems, handheld devices, networking devices or any other device that incorporates hard-wired and/or program logic to implement the techniques.

For example, FIG. 3 is a block diagram that illustrates a computer system 300 upon which an embodiment of the invention may be implemented. Computer system 300 includes a bus 302 or other communication mechanism for communicating information, and a hardware processor 304 coupled with bus 302 for processing information. Hardware processor 304 may be, for example, a general purpose microprocessor.

Computer system 300 also includes a main memory 306, such as a random access memory (RAM) or other dynamic storage device, coupled to bus 302 for storing information and instructions to be executed by processor 304. Main memory 306 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 304. Such instructions, when stored in non-transitory storage media accessible to processor 304, render computer system 300 into a special-purpose machine that is customized to perform the operations specified in the instructions.

Computer system 300 further includes a read only memory (ROM) 308 or other static storage device coupled to bus 302 for storing static information and instructions for processor 304. A storage device 310, such as a magnetic disk, optical disk, or solid-state drive is provided and coupled to bus 302 for storing information and instructions.

Computer system 300 may be coupled via bus 302 to a display 312, such as an OLED, LED or cathode ray tube (CRT), for displaying information to a computer user. An input device 314, including alphanumeric and other keys, is coupled to bus 302 for communicating information and command selections to processor 304. Another type of user input device is cursor control 316, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to processor 304 and for controlling cursor movement on display 312. This input device typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allows the device to specify positions in a plane. The input device 314 may also have multiple input modalities, such as multiple 2-axes controllers, and/or input buttons or keyboard. This allows a user to input along more than two dimensions simultaneously and/or control the input of more than one type of action.

Computer system 300 may implement the techniques described herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware and/or program logic which in combination with the computer system causes or programs computer system 300 to be a special-purpose machine. According to some embodiments, the techniques herein are performed by computer system 300 in response to processor 304 executing one or more sequences of one or more instructions contained in main memory 306. Such instructions may be read into main memory 306 from another storage medium, such as storage device 310. Execution of the sequences of instructions contained in main memory 306 causes processor 304 to perform the process steps described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions.

The term “storage media” as used herein refers to any non-transitory media that store data and/or instructions that cause a machine to operate in a specific fashion. Such storage media may comprise non-volatile media and/or volatile media. Non-volatile media includes, for example, optical disks, magnetic disks, or solid-state drives, such as storage device 310. Volatile media includes dynamic memory, such as main memory 306. Common forms of storage media include, for example, a floppy disk, a flexible disk, hard disk, solid-state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip or cartridge.

Storage media is distinct from but may be used in conjunction with transmission media. Transmission media participates in transferring information between storage media. For example, transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise bus 302. Transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infra-red data communications.

Various forms of media may be involved in carrying one or more sequences of one or more instructions to processor 304 for execution. For example, the instructions may initially be carried on a magnetic disk or solid-state drive of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to computer system 300 can receive the data on the telephone line and use an infra-red transmitter to convert the data to an infra-red signal. An infra-red detector can receive the data carried in the infra-red signal and appropriate circuitry can place the data on bus 302. Bus 302 carries the data to main memory 306, from which processor 304 retrieves and executes the instructions. The instructions received by main memory 306 may optionally be stored on storage device 310 either before or after execution by processor 304.

Computer system 300 also includes a communication interface 318 coupled to bus 302. Communication interface 318 provides a two-way data communication coupling to a network link 320 that is connected to a local network 322. For example, communication interface 318 may be an integrated services digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, communication interface 318 may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links may also be implemented. In any such implementation, communication interface 318 sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information. Such a wireless link could be a Bluetooth, Bluetooth Low Energy (BLE), 802.11 WiFi connection, or the like.

Network link 320 typically provides data communication through one or more networks to other data devices. For example, network link 320 may provide a connection through local network 322 to a host computer 324 or to data equipment operated by an Internet Service Provider (ISP) 326. ISP 326 in turn provides data communication services through the world wide packet data communication network now commonly referred to as the “Internet” 328. Local network 322 and Internet 328 both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on network link 320 and through communication interface 318, which carry the digital data to and from computer system 300, are example forms of transmission media.

Computer system 300 can send messages and receive data, including program code, through the network(s), network link 320 and communication interface 318. In the Internet example, a server 330 might transmit a requested code for an application program through Internet 328, ISP 326, local network 322 and communication interface 318.

The received code may be executed by processor 304 as it is received, and/or stored in storage device 310, or other non-volatile storage for later execution.

In the foregoing specification, embodiments of the invention have been described with reference to numerous specific details that may vary from implementation to implementation. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense. The sole and exclusive indicator of the scope of the invention, and what is intended by the applicants to be the scope of the invention, is the literal and equivalent scope of the set of claims that issue from this application, in the specific form in which such claims issue, including any subsequent correction. 

What is claimed is:
 1. A method comprising: performing the following until there are no more cases in a computer-based reasoning model with missing fields for which imputation is desired: determining which cases have fields to impute in the computer-based reasoning model; determining imputation order information for the cases that have data to impute in the computer-based reasoning model based at least in part on numbers of features that need imputed data for each of the cases; determining for which one or more cases with missing fields to impute data for the missing fields based on the imputation order information, wherein determining for which one or more cases with missing fields to impute data for the missing fields based on the imputation order information comprises: determining which one or more particular cases of the cases has a lowest number of features that need imputed data; and determining to impute data for the missing fields for the one or more particular cases that have the lowest number of features that need imputed data among the one or more particular cases; for each of the determined one or more particular cases with the lowest number of missing fields to impute: determining imputed data for a missing field of the missing fields based on the case, and an imputation model, and the missing fields in the case; modifying the case with the imputed data, wherein the modified case becomes part of the computer-based reasoning model in place of the original case to create an updated computer-based reasoning model, wherein the method is performed by one or more computing devices, causing, with a control system, control of a system with the updated computer-based reasoning model.
 2. The method of claim 1, wherein causing control of the system comprises: receiving a request for an action to take in the system, including a context for the system; determining the action to take based at least in part on the context for the system and the updated computer-based reasoning model; causing the control system to perform the determined action in the system.
 3. The method of claim 1, wherein the one or more particular cases with the highest number of missing fields to impute comprise two or more particular cases with the highest number of missing fields to impute; determining for which one or more cases with the missing fields to impute data for the missing fields based on the imputation order information further comprises: determining which of the two or more particular cases has a highest certainty score among the two or more particular cases, wherein the certainty scores is determined by a certainty function associated with: removing the case from the computer-based reasoning model; adding the case back into the computer-based reasoning model, wherein the certainty function is associated with a certainty that a particular set of data fits a model.
 4. The method of claim 1, wherein the one or more particular cases with the highest number of missing fields to impute comprise two or more particular cases with the highest number of missing fields to impute; determining for which one or more cases with the missing fields to impute data for the missing fields based on the imputation order information further comprises: determining which of the two or more particular cases has a highest certainty score among the two or more particular cases, wherein the certainty scores is determined by a certainty function associated with: removing the case from the computer-based reasoning model; adding the case back into the computer-based reasoning model, wherein the certainty function is associated with how much information a point distorts the model.
 5. The method of claim 1, wherein the one or more particular cases with the highest number of missing fields to impute comprise two or more particular cases with the highest number of missing fields to impute; determining for which one or more cases with the missing fields to impute data for the missing fields based on the imputation order information further comprises: determining which of the two or more particular cases has a highest certainty score among the two or more particular cases, wherein the certainty scores is determined by a certainty function associated with: removing the case from the computer-based reasoning model; adding the case back into the computer-based reasoning model, wherein the certainty function is associated with information required to describe the position of the point in question relative to existing points.
 6. The method of claim 1, wherein determining imputed data for the missing field comprises: determining the imputed data based on a machine learning model for the computer-based reasoning model's data, wherein the machine learning model for the computer-based reasoning model's data has been trained using the data in the computer-based reasoning model; and the method further comprises: determining an update to the machine learning model based on the updated computer-based reasoning model.
 7. One or more non-transitory storage media storing instructions which, when executed by one or more computing devices, cause performance of a method of: performing the following until there are no more cases in a computer-based reasoning model with missing fields for which imputation is desired: determining which cases have fields to impute in the computer-based reasoning model; determining imputation order information for the cases that have data to impute in the computer-based reasoning model based at least in part on numbers of features that need imputed data for each of the cases; determining for which one or more cases with missing fields to impute data for the missing fields based on the imputation order information, wherein determining for which one or more cases with missing fields to impute data for the missing fields based on the imputation order information comprises: determining which one or more particular cases of the cases has a lowest number of features that need imputed data, and determining to impute data for the missing fields for the one or more particular cases that have the lowest number of features that need imputed data among the one or more particular cases; for each of the determined one or more particular cases with the lowest number of missing fields to impute: determining imputed data for a missing field of the missing fields based on the case, and an imputation model, and the missing fields in the case; modifying the case with the imputed data, wherein the modified case becomes part of the computer-based reasoning model in place of the original case to create an updated computer-based reasoning model, causing, with a control system, control of a system with the updated computer-based reasoning model.
 8. The one or more non-transitory storage media of claim 7, wherein causing control of the system comprises: receiving a request for an action to take in the system, including a context for the system; determining the action to take based at least in part on the context for the system and the updated computer-based reasoning model; causing the control system to perform the determined action in the system.
 9. The one or more non-transitory storage media of claim 7, wherein the one or more particular cases with the highest number of missing fields to impute comprise two or more particular cases with the highest number of missing fields to impute; determining for which one or more cases with the missing fields to impute data for the missing fields based on the imputation order information further comprises: determining which of the two or more particular cases has a highest certainty score among the two or more particular cases, wherein the certainty scores is determined by a certainty function associated with: removing the case from the computer-based reasoning model; adding the case back into the computer-based reasoning model, wherein the certainty function is associated with a certainty that a particular set of data fits a model.
 10. The one or more non-transitory storage media of claim 7, wherein the one or more particular cases with the highest number of missing fields to impute comprise two or more particular cases with the highest number of missing fields to impute; determining for which one or more cases with the missing fields to impute data for the missing fields based on the imputation order information further comprises: determining which of the two or more particular cases has a highest certainty score among the two or more particular cases, wherein the certainty scores is determined by a certainty function associated with: removing the case from the computer-based reasoning model; adding the case back into the computer-based reasoning model, wherein the certainty function is associated with how much information a point distorts the model.
 11. The one or more non-transitory storage media of claim 7, wherein the one or more particular cases with the highest number of missing fields to impute comprise two or more particular cases with the highest number of missing fields to impute; determining for which one or more cases with the missing fields to impute data for the missing fields based on the imputation order information further comprises: determining which of the two or more particular cases has a highest certainty score among the two or more particular cases, wherein the certainty scores is determined by a certainty function associated with: removing the case from the computer-based reasoning model; adding the case back into the computer-based reasoning model, wherein the certainty function is associated with information required to describe the position of the point in question relative to existing points.
 12. The one or more non-transitory storage media of claim 7, wherein determining imputed data for the missing field comprises: determining the imputed data based on a machine learning model for the computer-based reasoning model's data, wherein the machine learning model for the computer-based reasoning model's data has been trained using the data in the computer-based reasoning model; and the method further comprises: determining an update to the machine learning model based on the updated computer-based reasoning model.
 13. A system comprising one or more computing devices, which one or more computing devices are configured to perform a method of: performing the following until there are no more cases in a computer-based reasoning model with missing fields for which imputation is desired: determining which cases have fields to impute in the computer-based reasoning model; determining imputation order information for the cases that have data to impute in the computer-based reasoning model based at least in part on numbers of features that need imputed data for each of the cases; determining for which one or more cases with missing fields to impute data for the missing fields based on the imputation order information, wherein determining for which one or more cases with missing fields to impute data for the missing fields based on the imputation order information comprises: determining which one or more particular cases of the cases has a lowest number of features that need imputed data, and determining to impute data for the missing fields for the one or more particular cases that have the lowest number of features that need imputed data among the one or more particular cases; for each of the determined one or more particular cases with the lowest number of missing fields to impute: determining imputed data for a missing field of the missing fields based on the case, and an imputation model, and the missing fields in the case; modifying the case with the imputed data, wherein the modified case becomes part of the computer-based reasoning model in place of the original case to create an updated computer-based reasoning model, causing, with a control system, control of a system with the updated computer-based reasoning model.
 14. The system of claim 13, wherein determining imputed data for the missing field comprises: determining the imputed data based on a machine learning model for the computer-based reasoning model's data, wherein the machine learning model for the computer-based reasoning model's data has been trained using the data in the computer-based reasoning model.
 15. The system of claim 13, wherein the one or more particular cases with the highest number of missing fields to impute comprise two or more particular cases with the highest number of missing fields to impute; determining for which one or more cases with the missing fields to impute data for the missing fields based on the imputation order information further comprises: determining which of the two or more particular cases has a highest certainty score among the two or more particular cases, wherein the certainty scores is determined by a certainty function associated with: removing the case from the computer-based reasoning model; adding the case back into the computer-based reasoning model, wherein the certainty function is associated with a certainty that a particular set of data fits a model.
 16. The system of claim 13, wherein the one or more particular cases with the highest number of missing fields to impute comprise two or more particular cases with the highest number of missing fields to impute; determining for which one or more cases with the missing fields to impute data for the missing fields based on the imputation order information further comprises: determining which of the two or more particular cases has a highest certainty score among the two or more particular cases, wherein the certainty scores is determined by a certainty function associated with: removing the case from the computer-based reasoning model; adding the case back into the computer-based reasoning model, wherein the certainty function is associated with how much information a point distorts the model.
 17. The system of claim 13, wherein the one or more particular cases with the highest number of missing fields to impute comprise two or more particular cases with the highest number of missing fields to impute; determining for which one or more cases with the missing fields to impute data for the missing fields based on the imputation order information further comprises: determining which of the two or more particular cases has a highest certainty score among the two or more particular cases, wherein the certainty scores is determined by a certainty function associated with: removing the case from the computer-based reasoning model; adding the case back into the computer-based reasoning model, wherein the certainty function is associated with information required to describe the position of the point in question relative to existing points. 